Integrating Population-Wide Laboratory Testing Data with Audit and Feedback Reports for Ontario Physicians
Bibliographic record
Abstract
Audit and feedback reports, distributed by Health Quality Ontario to consenting primary care physicians, provide doctors with a confidential summary of how they manage patients with diabetes; these reports currently lack clinical information. We examined the feasibility of linking the Ontario Laboratories Information System (OLIS), a large provincial database of laboratory test results, with the existing provincial audit and feedback reporting structure to integrate measures of glycemic and cholesterol control among patients with diabetes. We found that we could ascertain glycated hemoglobin (69.9%) and low-density lipoprotein cholesterol (64.1%) test results in the previous year for most patients and that there was wide variation among physicians in the proportion of patients who exceeded clinical thresholds for these measures. Our study highlights the potential value of reporting more clinically rich information to physicians to improve diabetes care and management and demonstrates the feasibility of using OLIS data at the population level to enhance ongoing research and quality improvement.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".