Politipatruljens forebyggende arbeid med vold i nære relasjoner : en teoretisk oppgave

Bachelor i politiutdannin

Feasibility study merging data from Deep Dive with other macrolitter analysis platforms

Prosjektleder: Jannike Falk-AnderssonThis study evaluates the technical feasibility of merging data used in the Deep Dive platform for the Arctic with a selection of established platforms for macrolitter registrations.Grid-ArendalpublishedVersio

Methods for determining the geographical origin and age of beach litter: Challenges and opportunities

Embargo until September 2, 2023.Beach litter analysis is a cost-effective tool to identify litter sources and subsequent management actions. However, standard beach litter protocols are not generally developed to identify litter's origins and age. Data from Svalbard (North Atlantic/ Arctic Ocean) were therefore used to explore reliable methods to fill this knowledge gap. Written text and country specific brands, as well as printed production or expiry dates proved the most efficient and reliable identifiers. The use of product design and logos considerably increased the proportion of items that could be sourced (by 19%) and dated (by 22%). The successful use of these is defined by the expertise of the analysing team and may introduce bias. The bias can be reduced by developing picture guides and involving stakeholders. The analyses showed that littering is on-going and that the area's major fishing nations, Norway and Russia, dominated the identified litter (38% and 14%, respectively).acceptedVersio

Significance of progesterone receptors (PR-A and PR-B) expression as predictors for relapse after successful therapy of endometrial hyperplasia: a retrospective cohort study

This is the peer reviewed version of the following article: Sletten, E.T., Arnes, M., Lyså, L.M., Larsen, M. & Ørbo, A. (2019). Significance of progesterone receptors (PR-A and PR-B) expression as predictors for relapse after successful therapy of endometrial hyperplasia: a retrospective cohort study. BJOG: an International Journal of Obstetrics and Gynaecology, 126(7), 936-943, which has been published in final form at https://doi.org/10.1111/1471-0528.15579. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.Objective - After successful progestin therapy for endometrial hyperplasia (EH), the risk of relapse remains. We aimed to assess if immunohistochemical (IHC) expression of progesterone receptor isoforms, PR‐A and PR‐B, in endometrial glands and stroma in pre‐treatment endometrial biopsies was related to relapse of EH. Design and setting - Biopsy material originated from women with low‐risk and medium‐risk EH recruited to a recent Norwegian multicentre randomised trial. Participants (n = 153) had been treated for 6 months with three different progestin regimens. Population - One hundred and thirty‐five of the 153 women achieved therapy response and underwent follow up for 24 months after therapy withdrawal. Fifty‐five women relapsed during follow up. Pre‐treatment endometrial biopsies from 94 of the 135 responding women were available for IHC staining. Methods - Immunohistochemical staining was performed separately for PR‐A and PR‐B and IHC expression was evaluated in endometrial glands and stroma by a histological score (H‐score) using light microscopy. Main outcome measure - Immunohistochemical expression of PR‐A and PR‐B in endometrial glands and stroma in women with or without relapse of EH. Results - Low PR‐A in endometrial glands (P = 0.013) and stroma (P 1 (19%; P < 0.001). Conclusion - Immunohistochemical expression of PR‐A and PR‐B in pre‐treatment endometrial biopsy proves valuable as a predictor of relapse in EH

Breast cancer missed at screening; hindsight or mistakes?

Purpose - To investigate radiologists’ interpretation scores of screening mammograms prior to diagnosis of screen-detected and interval breast cancers retrospectively classified as missed or true negative. Methods - We included data on radiologists’ interpretation scores at screening prior to diagnosis for 1223 screen-detected and 1007 interval cancer cases classified as missed or true negative in an informed consensus-based review. All prior screening examinations were independently scored 1–5 by two radiologists; score 1 by both was considered concordant negative, score ≥ 2 by one radiologist discordant, and score ≥ 2 by both concordant positive. We analyzed associations between interpretation, review categories, mammographic features and histopathological findings using descriptive statistics and logistic regression. Results - Among screen-detected cancers, 31% of missed and 10% of true negative cancers had discordant or concordant positive interpretation at prior screening. The corresponding percentages for interval cancer were 21% and 8%. Age-adjusted odds ratio (OR) and 95% confidence interval (CI) for missed screen-detected cancer was 3.8 (95% CI: 2.6–5.4) after discordant and 5.5 (95% CI: 3.2–9.5) after concordant positive interpretation, using concordant negative as reference. Corresponding ORs for missed interval cancer were 3.0 (95% CI: 2.0–4.5) for discordant and 6.3 (95% CI: 2.3–17.5) for concordant positive interpretation. Asymmetry was the dominating mammographic feature at prior screening for all, except concordant positive screen-detected cancers where a mass dominated. Histopathological characteristics did not vary statistically with interpretation. Conclusions - Most cancers were interpreted negatively at screening prior to diagnosis. Increased risk for missed screen-detected or interval cancer was observed after positive interpretation at prior screening

Artificial intelligence in BreastScreen Norway: a retrospective analysis of a cancer-enriched sample including 1254 breast cancer cases

Objectives To compare results of selected performance measures in mammographic screening for an artifcial intelligence (AI) system versus independent double reading by radiologists. Methods In this retrospective study, we analyzed data from 949 screen-detected breast cancers, 305 interval cancers, and 13,646 negative examinations performed in BreastScreen Norway during the period from 2010 to 2018. An AI system scored the examinations from 1 to 10, based on the risk of malignancy. Results from the AI system were compared to screening results after independent double reading. AI score 10 was set as the threshold. The results were stratifed by mammographic density. Results A total of 92.7% of the screen-detected and 40.0% of the interval cancers had an AI score of 10. Among women with a negative screening outcome, 9.1% had an AI score of 10. For women with the highest breast density, the AI system scored 100% of the screen-detected cancers and 48.6% of the interval cancers with an AI score of 10, which resulted in a sensitivity of 80.9% for women with the highest breast density for the AI system, compared to 62.8% for independent double reading. For women with screen-detected cancers who had prior mammograms available, 41.9% had an AI score of 10 at the prior screening round. Conclusions The high proportion of cancers with an AI score of 10 indicates a promising performance of the AI system, particularly for women with dense breasts. Results on prior mammograms with AI score 10 illustrate the potential for earlier detection of breast cancers by using AI in screen-reading

Reading intervention for students with intellectual disabilities without functional speech who require augmentative and alternative communication: a multiple single-case design with four randomized baselines

Background Literacy is one of the most important skills a students can achieve, as it provides access to information and communication. Unfortunately, literacy skills are not easily acquired, especially for students with intellectual disabilities who require augmentative and alternative communication (AAC). There are many barriers to literacy acquisition, some due to low expectations from parents and teachers and lack of evidence-based reading programs and reading materials adapted for AAC. Barriers as a result of extensive support needs is also a real factor. This trial aims to deliver reading instructions to 40 students with intellectual disabilities who require AAC and contribute in the debate on how to best support this population through reading instructions to maximizes their reading skills. Methodology Forty non-verbal or minimally verbal students (age 6–14) with intellectual disabilities who require AAC will be part of a reading intervention with a multiple single-case design with four randomized baselines. The intervention period will last for 18 months and will commence in March 2023. The students will receive the intervention in a one-to-one format, working systematically with a reading material that contains phonological awareness and decoding tasks based on the Accessible Literacy Learning (ALL) developed by Janice Light and David McNaughton. All the teachers will be trained to deliver the reading intervention. Discussion The reading material “Lesing for alle” (Reading for all) is based on and follow the strategies behind the research of ALL. The current trial will through a reading intervention contribute to move beyond only teaching sight words and combine several reading components such as sound blending, letter-sound correspondence, phoneme segmentation, shared reading, recognition of sight words, and decoding. The strategies and methods in use is built on the existing science of reading, especially what has been effective in teaching reading for students with intellectual disabilities who require AAC. There is limited generalizability of prior findings in reading-related phonological processing interventions to different populations of them who use AAC specially outside of the USA. More research is needed to understand how programs designed to improve reading skills across other settings understand the program’s long-term effects and to study the effectiveness when delivered by educators who are not speech language therapists or researchers.publishedVersio

Prolonged screening interval due to the COVID-19 pandemic and its association with tumor characteristics and treatment; a register-based study from BreastScreen Norway

Objective: During the COVID-19 pandemic Norway had to suspend its national breast cancer screening program. We aimed to investigate the effect of the pandemic-induced suspension on the screening interval, and its subsequent association with the tumor characteristics and treatment of screen-detected (SDC) and interval breast cancer (IC). Methods: Information about women aged 50–69, participating in BreastScreen Norway, and diagnosed with a SDC (N = 3799) or IC (N = 1806) between 2018 and 2021 was extracted from the Cancer Registry of Norway. Logistic regression was used to investigate the association between COVID-19 induced prolonged screening intervals and tumor characteristics and treatment. Results: Women with a SDC and their last screening exam before the pandemic had a median screening interval of 24.0 months (interquartile range: 23.8–24.5), compared to 27.0 months (interquartile range: 25.8–28.5) for those with their last screening during the pandemic. The tumor characteristics and treatment of women with a SDC, last screening during the pandemic, and a screening interval of 29–31 months, did not differ from those of women with a SDC, last screening before the pandemic, and a screening interval of 23–25 months. ICs detected 24–31 months after screening, were more likely to be histological grade 3 compared to ICs detected 0–23 months after screening (odds ratio: 1.40, 95% confidence interval: 1.06–1.84). Conclusions: Pandemic-induced prolonged screening intervals were not associated with the tumor characteristics and treatment of SDCs, but did increase the risk of a histopathological grade 3 IC. This study provides insights into the possible effects of extending the screening interval

Possible strategies for use of artificial intelligence in screen-reading of mammograms, based on retrospective data from 122,969 screening examinations

Objectives Artificial intelligence (AI) has shown promising results when used on retrospective data from mammographic screening. However, few studies have explored the possible consequences of different strategies for combining AI and radiologists in screen-reading. Methods A total of 122,969 digital screening examinations performed between 2009 and 2018 in BreastScreen Norway were retrospectively processed by an AI system, which scored the examinations from 1 to 10; 1 indicated low suspicion of malignancy and 10 high suspicion. Results were merged with information about screening outcome and used to explore consensus, recall, and cancer detection for 11 different scenarios of combining AI and radiologists. Results Recall was 3.2%, screen-detected cancer 0.61% and interval cancer 0.17% after independent double reading and served as reference values. In a scenario where examinations with AI scores 1–5 were considered negative and 6–10 resulted in standard independent double reading, the estimated recall was 2.6% and screen-detected cancer 0.60%. When scores 1–9 were considered negative and score 10 double read, recall was 1.2% and screen-detected cancer 0.53%. In these two scenarios, potential rates of screen-detected cancer could be up to 0.63% and 0.56%, if the interval cancers selected for consensus were detected at screening. In the former scenario, screen-reading volume would be reduced by 50%, while the latter would reduce the volume by 90%. Conclusion Several theoretical scenarios with AI and radiologists have the potential to reduce the volume in screen-reading without affecting cancer detection substantially. Possible influence on recall and interval cancers must be evaluated in prospective studies. Key Points Different scenarios using artificial intelligence in combination with radiologists could reduce the screen-reading volume by 50% and result in a rate of screen-detected cancer ranging from 0.59% to 0.60%, compared to 0.61% after standard independent double reading The use of artificial intelligence in combination with radiologists has the potential to identify negative screening examinations with high precision in mammographic screening and to reduce the rate of interval cancer</li

Personalized Breast Cancer Screening: A Risk Prediction Model Based on Women Attending BreastScreen Norway

Background: We aimed to develop and validate a model predicting breast cancer risk for women targeted by breast cancer screening. Method: This retrospective cohort study included 57,411 women screened at least once in BreastScreen Norway during the period from 2007 to 2019. The prediction model included information about age, mammographic density, family history of breast cancer, body mass index, age at menarche, alcohol consumption, exercise, pregnancy, hormone replacement therapy, and benign breast disease. We calculated a 4-year absolute breast cancer risk estimates for women and in risk groups by quartiles. The Bootstrap resampling method was used for internal validation of the model (E/O ratio). The area under the curve (AUC) was estimated with a 95% confidence interval (CI). Results: The 4-year predicted risk of breast cancer ranged from 0.22–7.33%, while 95% of the population had a risk of 0.55–2.31%. The thresholds for the quartiles of the risk groups, with 25% of the population in each group, were 0.82%, 1.10%, and 1.47%. Overall, the model slightly overestimated the risk with an E/O ratio of 1.10 (95% CI: 1.09–1.11) and the AUC was 62.6% (95% CI: 60.5–65.0%). Conclusions: This 4-year risk prediction model showed differences in the risk of breast cancer, supporting personalized screening for breast cancer in women aged 50–69 years