Management of drug-related problems including drug–drug interactions caused by nirmatrelvir/ritonavir in paediatric patients with SARS-CoV-2
Bibliographic record
Abstract
Aim As part of its strategic objectives for 2023, EULAR aims to improve the work participation of people with rheumatic and musculoskeletal diseases (RMDs). One strategic initiative focused on the development of overarching points to consider (PtC) to support people with RMDs in healthy and sustainable paid work participation. Methods EULAR’s standardised operating procedures were followed. A steering group identified six research areas on paid work participation. Three systematic literature reviews, several non-systematic reviews and two surveys were conducted. A multidisciplinary taskforce of 25 experts from 10 European countries and Canada formulated overarching principles and PtC after discussion of the results of literature reviews and surveys. Consensus was obtained through voting, with levels of agreement obtained anonymously. Results Three overarching principles and 11 PtC were formulated. The PtC recognise various stakeholders are important to improving work participation. Five PtC emphasise shared responsibilities (eg, obligation to provide active support) (PtC 1, 2, 3, 5, 6). One encourages people with RMDs to discuss work limitations when necessary at each phase of their working life (PtC 4) and two focus on the role of interventions by healthcare providers or employers (PtC 7, 8). Employers are encouraged to create inclusive and flexible workplaces (PtC 10) and policymakers to make necessary changes in social and labour policies (PtC 9, 11). A research agenda highlights the necessity for stronger evidence aimed at personalising work-related support to the diverse needs of people with RMDs. Conclusion Implementation of these EULAR PtC will improve healthy and sustainable work participation of people with RMDs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".