An advanced clinician practitioner in arthritis care can improve access to rheumatology care in community-based practice
Bibliographic record
Abstract
OBJECTIVE: To facilitate access and improve wait times to a rheumatologist's consultation, this study aimed to 1) determine the ability of an advanced clinician practitioner in arthritis care (ACPAC)-trained extended role practitioner (ERP) to triage patients with suspected inflammatory arthritis (IA) for priority assessment by a rheumatologist and 2) determine the impact of an ERP on access-to-care as measured by time-to-rheumatologist-assessment and time-to-treatment-decision. MATERIALS AND METHODS: A community-based ACPAC-trained ERP triaged new referrals for suspected IA. Patients with suspected IA were booked to see the rheumatologist on a priority basis. Diagnostic accuracy of the ERP to correctly identify priority patients; the level of agreement between ERP and rheumatologist (Kappa coefficient and percent agreement); and the time-to-treatment-decision for confirmed cases of IA were investigated. Retrospective chart review then compared time-to-rheumatologist-assessment and time-to-treatment-decision in the solo-rheumatologist versus the ERP-triage model. RESULTS: One hundred twenty-one patients were triaged. The ERP designated 54 patients for priority assessment. The rheumatologist confirmed IA in 49/54 (90.7% positive predictive value [PPV]). Of the 121 patients, 67 patients were designated as nonpriority by the ERP, and none were determined to have IA by the rheumatologist (100% negative predictive value [NPV]). Excellent agreement was found between the ERP and the rheumatologist (Kappa coefficient 0.92, 95% CI: 0.84-0.99). In the ERP-triage model, time-from-referral-to-treatment-decision for patients with IA was 73.7 days (SD 40.4, range 12-183) compared with 124.6 days (SD 61.7, range 26-359) in the solo-rheumatologist model (40% reduction in time-to-treatment-decision). CONCLUSION: A well-trained and experienced ERP can shorten the time-to-Rheumatologist-assessment and time-to-treatment-decision for patients with suspected IA.
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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 | 0.004 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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 | 0.001 |
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".