Triage of referrals to an outpatient rheumatology clinic: analysis of referral information and triage.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: Rheumatologists triage referrals in order to assess those patients who may benefit from early intervention. Success of triage strategies requires accurate transfer of clinical information between the primary caregiver and rheumatologist. We describe a prototype triage system and formally evaluate the quality of referral content to a rheumatologist's practice. METHODS: All new referrals were reviewed by a rheumatologist and, based on the information conferred, assigned a grade using a prototype triage system. This grade reflected each case's suspected urgency and guided the timing of consultation. After the initial rheumatologic consultation a post hoc grade was assigned to each case based on the clinical information gathered. Agreement between referral and consultation grades was assessed. All cases graded as urgent at the time of consultation, and thus felt to be truly urgent, were examined for the quality of content of their referral letters. RESULTS: Two hundred six referrals were evaluated. Ninety-six cases (47%) experienced a grade change between referral and consultation. Thirty-five cases (17%) were upgraded to urgent status after consultation, reflecting inappropriately triaged truly urgent patients. Analysis of referral letters for truly urgent cases revealed the absence of a presumptive diagnosis, symptom duration, and documentation of involved joints in over 30% of referrals. CONCLUSION: The absence of basic historical, examination, and laboratory markers accounted for inappropriate triage of urgent cases. Our study recognizes dysfunction within the current model of care and questions the development of standardized referral tools as a solution. Other models of care should be investigated for this patient population.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it