Application of the Ottawa Knee Rules in assessing acute knee injuries.
Bibliographic record
Abstract
The Ottawa Knee Rules (OKR) were established to identify which adults with acute knee injuries require knee x-rays as part of their assessment. This study evaluates the compliance of non-consultant hospital doctors (NCHDs), working in an Irish Emergency Dept., with these guidelines and assesses the impact of raising the profile of these rules on their implementation. Emergency Dept. (ED) notes of all adults who presented with an acute knee injury in a 3-month period were analysed retrospectively and compliance with the OKR was assessed. ED NCHDs were then educated on the details and value of these guidelines. In the subsequent three months, the improvement in compliance with the OKR was audited. In the initial audit, according to the Ottawa criteria, 65.5% of all x-rays of acute knee injuries were performed unnecessarily. In the second audit, performed after increasing awareness of the OKR, this figure had dropped to 39.1%. The NCHDs involved in this project cited 'patient expectation' for an x-ray as the primary reason why full compliance was not achieved. This study highlights a lack of awareness of and compliance with the OKR in the assessment of acute knee injuries in adults. It shows how the implementation of simple measures, which raised the profile of the OKR among ED staff, significantly improved compliance with the rules, thus cutting patient waiting times and cutting hospital costs. Futhermore, this study revealed that patients, when injured, expect to get x-rayed and ofter doctors comply with these expectations even if no indication exists.
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 | 0.021 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".