Modified Lemaire lateral tenodesis associated with revision anterior cruciate ligament reconstruction: a case series
Bibliographic record
Abstract
To increase the success and reduce failures related to anterior cruciate ligament reconstruction (ACLR), many techniques of lateral extra-articular tenodesis (LEAT) have been developed, mainly in revision surgeries, where a previous failure has already occurred. This study aims to report a case series of patients with failed ACLR treated with revision techniques combined with LEAT. Seven patients were retrospectively evaluated. At the postoperative analysis, in all patients, after six months of follow-up, there was no range of motion loss, there was an improvement in the functional Lysholm score, a reduction in the instability degree according to the semiological maneuvers of the anterior drawer and in pivot shift tests, compared to preoperative evaluation. No complications were observed. In conclusion, revision ACLR combined with LEAT showed good clinical functional results, without any complications reported.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".