Using pre-operative MRI to predict intraoperative hamstring graft size for anterior cruciate ligament reconstruction
Bibliographic record
Abstract
PURPOSE: Large variation in tendon size between individuals makes hamstring graft diameter for anterior cruciate ligament (ACL) reconstruction unpredictable. Inadequate graft diameter may necessitate an alternative source of tissue requiring pre-operative planning. The purpose of this study was to determine whether magnetic resonance image (MRI) measurements and clinical anthropometric data are predictive of hamstring tendon graft diameter. METHODS: Data from 109 patients having ACL reconstruction with semitendinosus-gracilis (STGT) autograft were retrospectively evaluated. Cross-sectional area (CSA) of the gracilis tendon (GT) and semitendinosus tendon (ST) were determined from pre-operative MRI scans. Variables included pre-operative height, weight, body mass index (BMI), age and gender; and intra-operative graft diameter. RESULTS: Correlations between anthropometric variables, hamstring tendons CSA and intra-operative graft diameter were calculated. Multiple stepwise regression was performed to assess the predictive value of these variables to graft diameter. Sensitivity and specificity were calculated to evaluate the utility of MRI CSA measurements in accurately identifying inadequate graft diameter (<8 mm). All anthropometric variables were positively correlated with intraoperative graft diameter (p < 0.01). Semitendinosus-gracilis tendon CSA (p < 0.001) and STGT CSA and weight (p < 0.001) were significantly predictive models of graft diameter. Sensitivity and specificity were 79 and 74 %, respectively. CONCLUSION: The strongest indicators of a four-stranded STGT graft for primary ACL reconstruction were STGT CSA on MRI plus weight. Measurement of graft diameter can be performed pre-operatively via MRI to identify tendons that may be of inadequate size for ACL reconstruction. This can assist with surgical planning to determine the most appropriate graft choice. 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".