Care pathways in early rheumatoid arthritis.
Bibliographic record
Abstract
OBJECTIVE: To determine the proportion of family physicians who diagnose rheumatoid arthritis (RA) correctly and to note how they report they would manage RA patients. DESIGN: Mailed survey (self-administered questionnaire) requesting comments on vignettes. SETTING: Province of Quebec. PARTICIPANTS: Computer-generated random sample of family physicians registered with the Quebec College of Family Physicians. MAIN OUTCOME MEASURES: The proportion of family physicians who recognized RA and their reported management strategies. RESULTS: Most respondents recognized the vignette presentation as a case of RA; 133/138 (96.4%) indicated RA as their provisional diagnosis, and all but 1 of the remaining respondents listed RA as a differential diagnosis. Of those who considered RA as a provisional or possible diagnosis, 107 (77.5% of all respondents) suggested referring the patient to a rheumatologist. Among the physicians who suggested referral, none indicated they would initiate disease-modifying antirheumatic drugs (DMARDs). CONCLUSION: Almost all respondents considered RA as a provisional or differential diagnosis. Although many suggested referring the patient to a rheumatologist, almost a quarter did not. Initiating DMARDs before referring patients to rheumatologists appears to be rare. Since DMARDs given during the early stages of RA are known to decrease damage and dysfunction, ways to increase their use and optimize care pathways for new-onset inflammatory arthritis are urgently needed.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.009 | 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".