Optimal decision criterion for detecting change in bone mineral density during serial monitoring: a Bayesian approach.
Bibliographic record
Abstract
UNLABELLED: Interpretation of change in serial bone densitometry using least significant change (LSC) may not lead to optimal decision making. Using the principles of Bayesian statistics and decision sciences, we developed the Optimal Decision Criterion (ODC) which resulted in 11-12.5% higher rate of correct classification compared with the LSC method. INTRODUCTION: The interpretation of change in serial bone densitometry emphasizes using least significant change (LSC) to distinguish between true changes and measurement error. METHODS: Using the principles of Bayesian statistics and decision sciences, we developed the optimal decision criterion (ODC) based on maximizing a 'utility' function that rewards the correct and penalizes the incorrect classification of change. The relationship between LSC and ODC is demonstrated using a clinical sample from the Manitoba Bone Density Program. RESULTS: Under certain conditions, it can be shown that using LSC at the 95% confidence level implicitly equates the benefit of 39 true positive diagnoses with the harm of one false positive classification of BMD change. ODC resulted in an 11% higher rate of correct classification for lumbar spine BMD change and a 12.5% better performance for classifying total hip BMD change compared with LSC with this method. CONCLUSIONS: ODC has the same clinical interpretation as LSC but with two major advantages: it can incorporate prior knowledge of the likely values of the true change and it can be fine-tuned based on the relative value placed on the correct and incorrect classifications. Bayesian statistics and decision sciences could potentially increase the yield of a BMD monitoring program.
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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 | 0.024 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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 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".