Multinational evidence-based recommendations for pain management by pharmacotherapy in inflammatory arthritis: integrating systematic literature research and expert opinion of a broad panel of rheumatologists in the 3e Initiative
Bibliographic record
Abstract
OBJECTIVE: To develop evidence-based recommendations for pain management by pharmacotherapy in patients with inflammatory arthritis (IA). METHODS: A total of 453 rheumatologists from 17 countries participated in the 2010 3e (Evidence, Expertise, Exchange) Initiative. Using a formal voting process, 89 rheumatologists representing all 17 countries selected 10 clinical questions regarding the use of pain medications in IA. Bibliographic fellows undertook a systematic literature review for each question, using MEDLINE, EMBASE, Cochrane CENTRAL and 2008-09 European League Against Rheumatism (EULAR)/ACR abstracts. Relevant studies were retrieved for data extraction and quality assessment. Rheumatologists from each country used this evidence to develop a set of national recommendations. Multinational recommendations were then formulated and assessed for agreement and the potential impact on clinical practice. RESULTS: A total of 49,242 references were identified, from which 167 studies were included in the systematic reviews. One clinical question regarding different comorbidities was divided into two separate reviews, resulting in 11 recommendations in total. Oxford levels of evidence were applied to each recommendation. The recommendations related to the efficacy and safety of various analgesic medications, pain measurement scales and pain management in the pre-conception period, pregnancy and lactation. Finally, an algorithm for the pharmacological management of pain in IA was developed. Twenty per cent of rheumatologists reported that the algorithm would change their practice, and 75% felt the algorithm was in accordance with their current practice. CONCLUSIONS: Eleven evidence-based recommendations on the management of pain by pharmacotherapy in IA were developed. They are supported by a large panel of rheumatologists from 17 countries, thus enhancing their utility in clinical practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.405 | 0.540 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.009 | 0.014 |
| Bibliometrics | 0.040 | 0.021 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.008 | 0.014 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".