Can We Call a Model of Care a “Model” If We Cannot Measure Its Performance?
Bibliographic record
Abstract
It has now been well established that early diagnosis and treatment of rheumatoid arthritis (RA) improve outcomes1.The barriers to early diagnosis and treatment are many but the most frequently cited are a shortage of rheumatologists and an increasing burden of inflammatory arthritis as the population increases and ages2,3. These barriers have prompted many groups across the country, at both the local and national levels, to develop innovative models of care (MOC) to improve access, diagnosis, and treatment for RA. Further, funding agencies such as The Canadian Institutes of Health Research4 and The Arthritis Society5 have research priorities in developing MOC for arthritis; policy makers are more than ever looking to optimize access and improve quality of care, and patients are demanding it as well. For an MOC to be a “model” for others to follow and implement, evaluation of its performance is key. The Arthritis Alliance of Canada (AAC) has developed 6 system-level performance measures to assess whether an MOC is effective in improving access to care and early treatment of inflammatory arthritis6. The term model of care is a frequently used term in our current healthcare landscape and it can carry different meanings in different contexts. Therefore, before we can evaluate these MOC we have … Address correspondence to Dr. N.K. Gakhal, Women’s College Hospital, 76 Grenville St., Room 3438, Toronto, Ontario M5S 1B2, Canada. E-mail: natasha.gakhal{at}wchospital.ca
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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 | 0.129 | 0.333 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.008 | 0.028 |
| Scholarly communication | 0.030 | 0.074 |
| Open science | 0.008 | 0.013 |
| Research integrity | 0.011 | 0.017 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".