Meniscal tears are more common than previously identified, however, less than a quarter of people with a tear undergo arthroscopy
Bibliographic record
Abstract
PURPOSE: The management of meniscal tears is a widely researched and evolving field. Previous studies reporting the incidence of meniscal tears are outdated and not representative of current practice. The aim of this study was to report the current incidence of MRI confirmed meniscal tears in patients with a symptomatic knee and the current intervention rate in a large NHS trust. METHODS: Radiology reports from 13,358 consecutive magnetic resonance imaging scans between 2015 and 2017, performed at a large UK hospital serving a population of 470,000, were assessed to identify patients with meniscal tears. The hospital database was interrogated to explore the subsequent treatment undertaken by the patient. A linear regression model was used to identify if any factors predicted subsequent arthroscopy. RESULTS: 1737 patients with isolated meniscal tears were identified in patients undergoing an MRI for knee pain, suggesting a rate of 222 MRI confirmed tears per 100,000 of the population aged 18 to 55 years old. 47% attended outpatient appointments and 22% underwent arthroscopy. Root tears [odds ratio (95% CI) 2.24 (1.0, 4.49); p = 0.049] and bucket handle tears were significantly associated with subsequent surgery, with no difference between the other types of tears. The presence of chondral changes did not significantly affect the rate of surgery [0.81 (0.60, 1.08); n.s]. CONCLUSION: Meniscal tears were found to be more common than previously described. However, less than half present to secondary care and only 22% undergo arthroscopy. These findings should inform future study design and recruitment strategies. In agreement with previous literature, bucket handle tears and root tears were significant predictors of subsequent surgery. LEVEL OF EVIDENCE: III.
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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 | 0.001 | 0.005 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".