Reproducibility and relative validity of dietary glycaemic index and glycaemic load assessed by the food-frequency questionnaire used in the Dutch cohorts of the European prospective investigation into Cancer and Nutrition

Limited information is available on the reproducibility and validity of dietary glycaemic index (GI) and glycaemic load (GL) estimated by habitual diet assessment methods such as FFQ, including the FFQ used in the Dutch cohorts of the European Prospective Investigation into Cancer and Nutrition study. To examine the reproducibility and relative validity of GI and GL, we used data from 121 Dutch men and women aged 23–72 years. They completed the FFQ three times at intervals of 6 months and twelve 24-h dietary recalls (24HDR) monthly during 1991–2. GI and GL were calculated using published values. Intra-class correlation coefficients of the three repeated FFQ were 0·78 for GI and 0·74 for GL. Pearson correlation coefficients between the first FFQ and the weighted average of the 24HDR were 0·63 for both GI and GL. Weighted ¿ values between the first FFQ and the average of the 24HDR (in quintiles) were 0·40 for GI and 0·41 for GL. Bland–Altman plots showed a proportional bias in GI (ß = 0·46), but not in GL (ß = 0·06). In conclusion, this FFQ can be used in epidemiological studies to investigate the relationship of GI and GL with disease risks, but the proportional bias should be taken into account when using this FFQ to assess the absolute GI values

A Test Collection of Synthetic Documents for Training Rankers:ChatGPT vs. Human Experts

We investigate the usefulness of generative large language models (LLMs) in generating training data for cross-encoder re-rankers in a novel direction: generating synthetic documents instead of synthetic queries. We introduce a new dataset, ChatGPT-RetrievalQA, and compare the effectiveness of strong models fine-tuned on both LLM-generated and human-generated data. We build ChatGPT-RetrievalQA based on an existing dataset, the human ChatGPT comparison corpus (HC3), consisting of multiple public question collections featuring both human- and ChatGPT-generated responses. We fine-tune a range of cross-encoder re-rankers on either human-generated or ChatGPT-generated data. Our evaluation on MS MARCO DEV, TREC DL'19, and TREC DL'20 demonstrates that cross-encoder re-ranking models trained on LLM-generated responses are significantly more effective for out-of-domain re-ranking than those trained on human responses. For in-domain re-ranking, however, the human-trained re-rankers outperform the LLM-trained re-rankers. Our novel findings suggest that generative LLMs have high potential in generating training data for neural retrieval models and can be used to augment training data, especially in domains with less labeled data. ChatGPT-RetrievalQA presents various opportunities for analyzing and improving rankers with both human- and LLM-generated data. Our data, code, and model checkpoints are publicly available.</p

The Effectiveness of Desensitization Therapy for Individuals with Complex Regional Pain Syndrome: A Systematic Review

Authors: Kira L, Donnelly, SPT; Lauryn M. Helmers, SPT; Olivia M. Verberne, SPT; Roger J. Allen, PT, PhD Title: The Effectiveness of Desensitization Therapy for Individuals with Complex Regional Pain Syndrome: A Systematic Review Purpose: Systematically review evidence supporting the use of desensitization therapy to treat Complex Regional Pain Syndrome (CRPS). Subjects: This systematic review evaluated 10 studies from peer-reviewed journals fitting research criteria. Materials/Methods: Databases were searched between Mar and Aug of 2014 with the following search terms: complex regional pain syndrome, CRPS, allodynia, desensitization, neuropathic pain, physical therapy, tactile desensitization, pressure desensitization, hydrotherapy, physiotherapy, capsaicin and somatosensory. Results: Initial search yielded 42 articles with 10 fitting inclusion/exclusion criteria. Articles were evaluated with the STROBE scale and organized by desensitization type: chemical, tactile, thermal and pressure desensitization. Outcome measures varied, including assessing functional use, pressure tolerance and pain tolerance. Conclusions: Despite lacking a standard desensitization protocol, research suggests implementing desensitization by selecting the proper somatosensory modality and using a graded protocol in order to reduce allodynia. Clinical Relevance: Desensitization is often a component of a multifaceted treatment approach for patients with CRPS, which is difficult to isolate within research. To make solid conclusions about desensitization efficacy, studies need to isolate desensitization as a treatment using larger numbers of subjects with CRPS with clear, controlled and replicable protocols. Given current research limitations, existing evidence is promising for continued utilization of graded desensitization therapy for individuals with CRPS. Keywords: desensitization, complex regional pain syndrome, allodynia, tactile desensitization, pressure desensitization, hydrotherapy, capsaicin, physical therap

CLosER: Conversational Legal Longformer with Expertise-Aware Passage Response Ranker for Long Contexts

In this paper, we investigate the task of response ranking in conversational legal search. We propose a novel method for conversational passage response retrieval (ConvPR) for long conversations in domains with mixed levels of expertise. Conversational legal search is challenging because the domain includes long, multi-participant dialogues with domain-specific language. Furthermore, as opposed to other domains, there typically is a large knowledge gap between the questioner (a layperson) and the responders (lawyers), participating in the same conversation. We collect and release a large-scale real-world dataset called LegalConv with nearly one million legal conversations from a legal community question answering (CQA) platform. We address the particular challenges of processing legal conversations, with our novel Conversational Legal Longformer with Expertise-Aware Response Ranker, called CLosER. The proposed method has two main innovations compared to state-of-the-art methods for ConvPR: (i) Expertise-Aware Post-Training; a learning objective that takes into account the knowledge gap difference between participants to the conversation; and (ii) a simple but effective strategy for re-ordering the context utterances in long conversations to overcome the limitations of the sparse attention mechanism of the Longformer architecture. Evaluation on LegalConv shows that our proposed method substantially and significantly outperforms existing state-of-the-art models on the response selection task. Our analysis indicates that our Expertise-Aware Post-Training, i.e., continued pre-training or domain/task adaptation, plays an important role in the achieved effectiveness. Our proposed method is generalizable to other tasks with domain-specific challenges and can facilitate future research on conversational search in other domains.</p

Intermediate weight changes and follow-up of dietetic treatment in primary healthcare:An observational study

Background Primary health care data have shown that most patients who were treated for overweight or obesity by a dietitian did not accomplish the recommended treatment period. It is hypothesised that a slow rate of weight loss might discourage patients from continuing dietetic treatment. This study evaluated intermediate weight changes during regular dietetic treatment in Dutch primary health care, and examined whether weight losses at previous consultations were associated with attendance at follow-up consultations. Methods This observational study was based on real life practice data of overweight and obese patients during the period 2013–2017, derived from Dutch dietetic practices that participated in the Nivel Primary Care Database. Multilevel regression analyses were conducted to estimate the mean changes in body mass index (BMI) during six consecutive consultations and to calculate odds ratios for the association of weight change at previous consultations with attendance at follow-up consultations. Results The total study population consisted of 25,588 overweight or obese patients, with a mean initial BMI of 32.7 kg/m2. The BMI decreased between consecutive consultations, with the highest weight losses between the first and second consultation. After six consultations, a mean weight loss of − 1.5 kg/m2 was estimated. Patients who lost weight between the two previous consultations were more likely to attend the next consultation than patients who did not lose weight or gained weight. Conclusions Body mass index decreased during consecutive consultations, and intermediate weight losses were associated with a higher attendance at follow-up consultations during dietetic treatment in overweight patients. Dietitians should therefore focus on discussing intermediate weight loss expectations with their patients

Achievement of weight loss in patients with overweight during dietetic treatment in primary health care

INTRODUCTION:Dietitians are the preferred primary health care professionals for nutritional care in overweight patients. Guidelines for dietitians recommend a weight reduction of ≥ 5% of initial body weight after one year of treatment. The purpose of this study was to evaluate weight change in patients with overweight who were treated by dietitians in Dutch primary health care, and to identify patient characteristics that were associated with it. MATERIALS AND METHODS:This observational study data was based on real life practice data of patients with overweight during the period 2013-2017, derived from dietetic practices that participated in the Nivel Primary Care Database. Multilevel linear regression analyses were performed to investigate weight change after dietetic treatment and to explore associations with patient characteristics. RESULTS:In total, data were evaluated from 56 dietetic practices and 4722 patients with a body mass index (BMI) ≥ 25 kg/m2. The mean treatment time was 3 hours within an average timeframe of 5 months. Overall, patients had a mean weight change of -3.5% (95% CI: -3.8; -3.1) of their initial body weight, and a quarter of the patients reached a weight loss of 5% or more, despite the fact that most patients did not meet the recommended treatment duration of at least one year. The mean BMI change was -1.1 kg/m2 (95% CI: -1.2; -1.0). Higher weight reductions were shown for patients with a higher initial BMI and for patients with a longer treatment time. Sex and age were not associated with weight change, and patients with other dietetic diagnoses, such as diabetes, hypertension, and hypercholesterolemia, had lower weight reductions. CONCLUSIONS:This study showed that dietetic treatment in primary health care coincided with modest weight reduction in patients with overweight. The weight loss goals were not reached for most patients, which was possibly due to a low treatment adherence

WN-BERT: integrating WordNet and BERT for lexical semantics in natural language understanding

Computer Systems, Imagery and Medi

ChiSCor: A Corpus of Freely Told Fantasy Stories by Dutch Children for Computational Linguistics and Cognitive Science

In this resource paper we release ChiSCor, a new corpus containing 619 fantasy stories, told freely by 442 Dutch children aged 4-12. ChiSCor was compiled for studying how children render character perspectives, and unravelling language and cognition in development, with computational tools. Unlike existing resources, ChiSCor's stories were produced in natural contexts, in line with recent calls for more ecologically valid datasets. ChiSCor hosts text, audio, and annotations for character complexity and linguistic complexity. Additional metadata (e.g. education of caregivers) is available for one third of the Dutch children. ChiSCor also includes a small set of 62 English stories. This paper details how ChiSCor was compiled and shows its potential for future work with three brief case studies: i) we show that the syntactic complexity of stories is strikingly stable across children's ages; ii) we extend work on Zipfian distributions in free speech and show that ChiSCor obeys Zipf's law closely, reflecting its social context; iii) we show that even though ChiSCor is relatively small, the corpus is rich enough to train informative lemma vectors that allow us to analyse children's language use. We end with a reflection on the value of narrative datasets in computational linguistics.Comment: 12 pages, 5 figures, forthcoming in Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL