Development of a Multidimensional Additive Points System for Determining Access to Rheumatology Services
Bibliographic record
Abstract
OBJECTIVE: In many countries, including New Zealand, the demand for rheumatology services exceeds their supply, resulting in some patients experiencing long delays or being denied access. The principal aim of this work was to create a validated, transparent, and fair system for determining access to rheumatology services. METHODS: A panel of 5 rheumatologists, 6 primary care physicians, and 4 nurse specialists ranked a series of 25 clinical scenarios in order of priority to see a rheumatologist. Important determining factors were weighted in an iterative process to generate a multidimensional additive point score to determine access to rheumatology service. RESULTS: The score comprises 6 domains of 2 to 4 items weighted to give a total score out of 100. The effect of the problem on the patient's life and role, the presence of an inflammatory rheumatic disease, appropriateness of current treatment, and the ability of the rheumatologist to influence the current symptoms and future prognosis were felt to be critical factors in determining access to the service. The score showed a strong correlation with the rankings agreed by the clinical panel, and the overall intraclass correlation coefficient for the rheumatologists was 0.698. CONCLUSIONS: Our score has face validity, is easy to perform, and has been assessed by an independent panel of rheumatologists as providing a fair system for determining access to rheumatology services. The system is acceptable to primary care physicians and has been adopted by our local primary care organizations.
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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 | 0.016 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".