Detection of Temporal Clinical Events in Non-Temporal, Non-Annotated Data
Bibliographic record
Abstract
INTRODUCTION: This study developed and validated a Bayesian method which detects pairs of clinical events likely to be temporally ordered. METHODS: Association mining rules between medical procedures were extracted into a research-generated database and, for each pair of procedures A,B, the conditional probability P(A|B), its inverse, and the difference from its inverse (ConfDiff) were calculated. The study hypothesized that the higher the ConfDiff is, the more likely it is for A and B to be temporally ordered. The actual calendar date of each medical procedure served as ground truth. RESULTS: ConfDiff is the strongest predictor of %Tseq (r=0.278), followed by P(B|A) (r=0.129). This association continued to be present after controlling for the confidence, leverage and conviction metrics. CONCLUSION: Findings substantiate the assumption that, in a structured process-based domain (e.g., clinical care) if an attribute is strongly associated with another one, but not the other way around, this could imply temporality.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".