Use of a Clinical Prediction Score in Patients with Suspected Deep Venous Thrombosis: Two Steps Forward, One Step Back?
Bibliographic record
Abstract
Since antiquity, a measure of a physician’s skill and ex-perience has been the ability to predict at the bedside whether a disease is present or absent. In the current era, standardized clinical prediction scores (also known as clin-ical decision rules) can level the playing field so that phy-sicians, irrespective of experience, can obtain an estimate of disease likelihood (1, 2). Clinical prediction scores can guide clinical management (for example, Ottawa ankle/ knee rules), estimate adverse outcome risk (for example, Detsky score), and determine prognosis (for example, Acute Physiology and Chronic Health Evaluation [APACHE] score). The increasing availability of electronic medical records facilitates the use of clinical prediction scores: After a physician enters the chief complaint or clin-ical scenario, the computer can display the appropriate clinical prediction score, calculate the score, display the
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".