Adherence to Analgesics for Cancer Pain: A Comparative Study of African Americans and Whites Using an Electronic Monitoring Device

Despite well-documented disparities in cancer pain outcomes among African Americans, surprisingly little research exists on adherence to analgesia for cancer pain in this group. We compared analgesic adherence for cancer-related pain over a 3-month period between African Americans and whites using the Medication Event Monitoring System (MEMS). Patients (N = 207) were recruited from outpatient medical oncology clinics of an academic medical center in Philadelphia (≥18 years of age, diagnosed with solid tumors or multiple myeloma, with cancer-related pain, and at least 1 prescription of oral around-the-clock analgesic). African Americans reported significantly greater cancer pain (P \u3c .001), were less likely than whites to have a prescription of long-acting opioids (P \u3c .001), and were more likely to have a negative Pain Management Index (P \u3c .001). There were considerable differences between African Americans and whites in the overall MEMS dose adherence, ie, percentage of the total number of prescribed doses that were taken (53% vs 74%, P \u3c .001). On subanalysis, analgesic adherence rates for African Americans ranged from 34% (for weak opioids) to 63% (for long-acting opioids). Unique predictors of analgesic adherence varied by race; income levels, analgesic side effects, and fear of distracting providers predicted analgesic adherence for African Americans but not for whites. Perspective: Despite evidence of disparities in cancer pain outcomes among African Americans, surprisingly little research exists on African Americans\u27 adherence to analgesia for cancer pain. This prospective study uses objective measures to compare adherence to prescribed pain medications between African American and white patients with cancer pain

Validation of the Dutch Version of the Breakthrough Pain Assessment Tool in Patients With Cancer

Context: Essential for adequate management of breakthrough cancer pain is a combination of accurate (re-)assessment and a personalized treatment plan. The Breakthrough Pain Assessment Tool (BAT) has been proven to be a brief, multidimensional, reliable, and valid questionnaire for the assessment of breakthrough cancer pain. Objectives: The aim of this study was to examine the validity and reliability of the Dutch Language version of the BAT (BAT-DL) in patients with cancer. Methods: The BAT was forward-backward translated into the Dutch language. Thereafter, the psychometric properties of the BAT-DL were tested, that is factor structure, reliability (internal consistency and test-retest reliability), validity (content validity and construct validity), and the responsiveness to change. Results: The BAT-DL confirmed the two-factor structure in 170 patients with cancer: pain severity/impact factor and pain duration/medication efficacy factor. The Cronbach's alpha coefficient was 0.72, and the intraclass correlation for the test-retest reliability was 0.81. The BAT-DL showed to be able to differentiate between different group of patients and correlated significantly with the Brief Pain Inventory. In addition, the BAT-DL was capable to detect clinically important changes over time. Conclusion: The BAT-DL is a valid and reliable questionnaire to assess breakthrough pain in Dutch patients with cancer and is a relevant questionnaire for daily practice

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency–Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe