Prevalence and consequences of delayed diagnosis of anterior cruciate ligament ruptures
Bibliographic record
Abstract
PURPOSE: Rupture of the anterior cruciate ligament (ACL) is a common injury, often presenting with a typical injury pattern. Historically, the literature indicates that the accuracy of diagnosis of ACL ruptures is poor at the initial medical consultation. The aims of this study were to determine: the mechanism of injury; changes in accuracy of diagnosis of ACL ruptures at initial presentation over the last decade; and the effect of subsequent delay in diagnosis and definitive treatment. METHODS: A prospective cohort of one hundred and thirty-two consecutive patients who underwent ACL reconstruction between 2005 and 2009 were analysed. The median age of the patients was 18 years (12-57). Sixteen patients were excluded due to chronic ACL injury. RESULTS: One hundred and sixteen patients (117 ACL ruptures) were included in the analysis. A typical injury pattern was documented in 87 (74.4 %) of cases. The most common sporting activities associated with an ACL injury were football (35.3 %), skiing (21.6 %) and rugby (10.3 %). The majority of patients (67.5 %) sought medical attention within 1 week from time of injury. The correct diagnosis of an ACL rupture was made in 33 cases (28.2 %) at the initial medical consultation. The diagnosis was made following medical consultation in 13 (11.1 %) of cases with the use of magnetic resonance imaging and 6 (5.1 %) cases at arthroscopy. The median time to diagnosis was 6 weeks (0-192), and the median time to ACL reconstruction was 24 weeks (1-240). A delay in diagnosis of >6 months was associated with a medial meniscal tear rate of 72.2 % compared to 23.1 % if the diagnosis was made within 4 months of the injury (p < 0.05). CONCLUSIONS: Despite a 'typical' mechanism of injury leading to ACL rupture, the rate of initial diagnosis in the UK still remains poor. This often leads to an unnecessary delay in the diagnosis and subsequent treatment and increases the risk of secondary injury to the knee. A delay in diagnosis of >6 months was associated with an increased medial meniscal tear rate. Patients who present with a 'typical' injury pattern should therefore be referred for further assessment by a knee specialist within 6 weeks. LEVEL OF EVIDENCE: IV.
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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".