Thirty-Year Experience With ACL Reconstruction Using Patellar Tendon: A Critical Evaluation of Revision and Reoperation
Bibliographic record
Abstract
BACKGROUND: During the preoperative discussion prior to anterior cruciate ligament reconstruction (ACLR), patients are often interested in data regarding rates of revision reconstruction, reoperation, concomitant pathologic changes, and future contralateral ACL injury. PURPOSE: To analyze a single surgeon's experience with primary and revision ACLR over a 30-year interval, focusing on incidence and risk factors for revision and reoperation. STUDY DESIGN: Case series; Level of evidence, 4. METHODS: Patients who underwent ACLR from 1986 to 2016 were identified from a prospectively maintained database. Covariates of interest included age, sex, time, and graft selection. Outcomes of interest included revision and reoperation rates. RESULTS: A total of 2450 ACLRs (mean patient age, 29 years; 58% male) were reviewed. Among primary ACLRs performed (n = 2225), 68% entailed bone-patellar tendon-bone (BTB) autograft and 30% entailed BTB allograft. Patients undergoing ACLR with autograft and allograft had a mean age of 22 and 37 years, respectively. The rate of revision ACLR was 1.8% and 3.5% for primary and revision cases, respectively. An increased rate of revision was noted among females compared with males (2.6% vs 1.2%) and among allografts compared with autografts (2.7% vs 1.3%). Low-dose irradiation did not affect allograft revision rates. The nonrevision reoperation rate following primary ACLR was 12%. The nonrevision reoperation rate was lower among primary cases reconstructed with allograft than autograft (9% vs 13%). Seventeen percent of cases involved concomitant meniscal repair and, among these, 13% required revision meniscal surgery. The rate of contralateral ACLR was 5.3. CONCLUSION: This information is useful in the informed consent process, for perioperative decision making regarding graft choice, and for identifying patients who are at risk for injuring the uninvolved knee.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".