When Your Clinical Examination Is as Good as an X-Ray Film
Bibliographic record
Abstract
OBJECTIVE. The Ottawa Knee Rule (see Need for Knee X-Ray Films) was derived and validated as a prediction rule that would clinically detect 100 % of fractures sustained in acute knee injury. This study was done to determine how implementing the rule in clinical practice affects use of radiography. Other objectives were to further validate the accuracy and reliability of the rule and to assess the impact of its use on waiting times and medical charges. DESIGN. Nonrandomized, controlled clinical trial that included before-and-after comparisons of patients seen in the emergency departments of two intervention hos-pitals (one community hospital and one teaching hospital) and two control hospitals in Ontario, Canada. PATIENTS. Patients were adults who had sustained acute knee injury from any cause and were seen at one of the intervention hospitals. Controls were adults who sus-tained acute knee injury and were seen at one of the two control hospitals. Patients who were younger than 18 years of age, were pregnant, had sustained injury more than 7 days before evaluation, had returned for reassessment of an injury, had altered
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".