The Birmingham Mandible and Mid-face (BruMM) rules: interim data analysis
Bibliographic record
Abstract
INTRODUCTION: Clinical predictor rules are useful heuristics that can inspire confidence in clinicians on the front line to make decisions that are safe and reproducible. Rules such as the Ottawa Ankle Rules can also reduce the number of unnecessary radiographs taken, reducing radiation exposure and cost, as well as improving quality of care. METHODS: A previous Delphi study delineated 11 variables associated with an increased likelihood of finding a mandibular fracture and 14 variables associated with an increased likelihood of finding a zygomatic fracture on plain film radiographs. In the current study, clinicians suspecting a mandibular and/or zygomatic fracture were invited to complete a proforma identifying any of these variables in advance of requesting plain film radiograph(s). An interim analysis was conducted with predictors being cross-tabulated against relevant outcomes using: sensitivity, specificity, Jaccard index, odds ratio (OR) and Fisher's exact probability. RESULTS: <0.05, OR undefined). CONCLUSIONS: =252 will be required, assuming a negative scan rate of 55%, to achieve a specificity of 0.90 within ±0.05. We aim to present finalised data in 2025.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".