ACL graft selection: state of the art
Bibliographic record
Abstract
Despite recent developments in anterior cruciate ligament (ACL) reconstruction techniques, there are still several intraoperative factors affecting clinical outcomes that remain widely debated. Among such factors, graft selection might be the most critical yet controversial question for surgeons. As the primary factor influencing a patient's choice for the ACL graft is surgeon recommendation, surgeons have to consider several factors to select the best graft for each patient. Graft options currently include autograft, allograft or synthetic grafts. In terms of autograft, there are three main options: hamstring tendon, bone-patellar tendon-bone (BPTB) and quadriceps tendon, the two most commonly used being hamstring tendon and BPTB. Limited evidence is available to select the one best graft for every individual patient. Graft selection should be based on the reported rate of graft failure/revision and be individualised according to multiple factors such as gender, age, activity level and type of activity, complications and other patient needs and demands. Furthermore, surgeons should be familiar with a variety of grafts, their specific associated surgical procedures and the advantages and disadvantages of each, with the aim of offering the best graft selection for each individual patient.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".