Relative urgency for referral from primary care to rheumatologists: The Priority Referral Score
Bibliographic record
Abstract
OBJECTIVE: Timely access to rheumatology consultation is fundamental to appropriate and effective management of patients with musculoskeletal and autoimmune diseases. Yet, for a variety of reasons, limited and delayed access is commonplace. Moreover, information exchange for referral is often inadequate or poorly communicated. The objective of this work was to improve referral from primary care to rheumatology by formulating and testing a clinically coherent, reliable, and non-diagnosis-dependent Priority Referral Score (PRS). METHODS: Using a deliberative process, a clinical panel of 10 primary care providers (PCPs) and rheumatology specialists reviewed clinical case scenarios and engaged in a highly iterative process to develop criteria, definitions, and weights for the PRS, a linear 100-point scale to rate the relative urgency of referral. Following tool formulation, clinicians uninvolved with the process tested the PRS against their clinical judgment. RESULTS: The PRS comprises 8 criteria, with 2-4 levels for each criterion, and each having a weight generated through conjoint analysis, which forced choices around the comparative urgency of all of the criteria and levels. The PRS showed a strong correlation between clinical rankings of rheumatologists and PCPs in both the deliberative panel, and the physicians subsequently involved in the testing of the PRS. CONCLUSION: No standardized priority-setting criteria are available for the full range of primary care referrals to rheumatologists. The PRS had face value with panelists and provided acceptable interrater and intrarater reliability when tested with other rheumatologists and PCPs. Pilot testing with other clinicians and in other settings is justified and prerequisite to use in clinical practice.
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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 | 0.019 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".