Using medicolegal data to support safe medical care: A contributing factor coding framework
Bibliographic record
Abstract
OBJECTIVE: Traditional medicolegal data analysis focuses on physician care, without a full acknowledgment of the effects of team, organizational, and system factors. We developed a patient safety-informed contributing factor framework to strengthen the coding and analysis of medicolegal data. MATERIALS AND METHODS: We incorporated patient safety theory and human factors science into our medicolegal case coding practices to improve our understanding of the many factors that contribute to medicolegal events. RESULTS AND DISCUSSION: A new framework was developed that has at its core, patients and their experience, and looks beyond the provider factors that are often the focus of medicolegal analysis to give greater consideration to the influence of team, organizational, and system factors. We anticipate that this substantial shift will strengthen our knowledge translation efforts to help improve the safety of medical care. CONCLUSION: We believe that reframing medicolegal case coding systems to better identify the influence of team, organizational, and system factors will increase the utility of this analysis in patient safety research, and health care 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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".