Quality assurance of sphincterotomy: A prospective single-centre survey
Bibliographic record
Abstract
BACKGROUND: In Ontario, Canada, the patient-centred medical home is a model of primary care delivery that includes 3 model types of interest for this study: enhanced fee-for-service, blended capitation, and team-based blended capitation. All 3 models involve rostering of patients and have similar practice requirements but differ in method of physician reimbursement, with the blended capitation models incorporating adjustments for age and sex, but not case mix, of rostered patients. We evaluated the extent to which persons with mental illness were included in physicians' total practices (as rostered and non-rostered patients) and were included on physicians' rosters across types of medical homes in Ontario. METHODS: Using population-based administrative data, we considered 3 groups of patients: those with psychotic or bipolar diagnoses, those with other mental health diagnoses, and those with no mental health diagnoses. We modelled the prevalence of mental health diagnoses and the proportion of patients with such diagnoses who were rostered across the 3 medical home model types, controlling for demographic characteristics and case mix. RESULTS: Compared with enhanced fee-for-service practices, and relative to patients without mental illness, the proportions of patients with psychosis or bipolar disorders were not different in blended capitation and team-based blended capitation practices (rate ratio [RR] 0.91, 95% confidence interval [CI] 0.82-1.01; RR 1.06, 95% CI 0.96-1.17, respectively). However, there were fewer patients with other mental illnesses (RR 0.94, 95% CI 0.90-0.99; RR 0.89, 95% CI 0.85-0.94, respectively). Compared with expected proportions, practices based on both capitation models were significantly less likely than enhanced fee-for-service practices to roster patients with psychosis or bipolar disorders (for blended capitation, RR 0.92, 95% CI 0.90-0.93; for team-based capitation, RR 0.92, 95% CI 0.88-0.93) and also patients with other mental illnesses (for blended capitation, RR 0.94, 95% CI 0.92-0.95; for team-based capitation, RR 0.93, 95% CI 0.92-0.94). INTERPRETATION: Persons with mental illness were under-represented in the rosters of Ontario's capitation-based medical homes. These findings suggest a need to direct attention to the incentive structure for including patients with mental illness.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".