Bibliographic record
Abstract
Abstract Acute knee injuries are a common presentation to the emergency department(ED). Ottawa knee rules (OKR) have shown to reduce the number of radiographs in these patients in North American studies and a fracture rate of 5% has been reported. Based on this, we tested the hypothesis that it was possible to decrease the number of x-ray films obtained after a knee trauma without delayed fracture diagnosis by means of the Ottawa knee rules in British set up. A total of 118 adult patients with acute Knee injuries were studied. A checklist in an easy-to-use format was produced to act as an aide-memoir and to encourage clinicians to apply the OKR in their decision making. Sixty patients were studied before introducing the check list stickers of OKR and fifty eight were studied after introducing the stickers. The OKR were found to have been obeyed in 24 (40%) of patients in the group which did not have stickers. In the group assessed without an OKR sticker, 28 (46.7%) of patients had knee radiography, compared with 29 (50%) of patients in the group who were assessed with an OKR sticker. There was no decrease in the number of x-rays after following the Ottawa Knee Rules (OKR).
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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 | 0.007 | 0.097 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".