Corrigendum to ‘No increase in adverse events with lateral extra-articular tenodesis augmentation of anterior cruciate ligament reconstruction – Results from the stability randomized trial’ [Journal of ISAKOS 8 (2023) 246–254]
Bibliographic record
Abstract
The authors regret there were some errors in Table 4. The article has been updated and the correct version of the table can also be found below.Table 4Adverse events recorded within the first 12-months post-operative and the total number of adverse events recorded within the entire 24-month period.12-months post-operative24-months post-operativeNo Adverse Events512 (83%)439 (71%)Minor Medical Adverse Events48 (8%)69 (11%) ACLR18 (6%)29 (9%) ACLR + LET30 (10%)40 (13%)Minor Surgical Events (excluding ACL tears)29 (5%)46 (7%) ACLR11 (4%)18 (6%) ACL + LET18 (6%)28 (9%)Contralateral ACL Tear7 (1%)19 (3%) ACLR6 (2%)12 (4%) ACL + LET1 (<1%)7 (2%)Graft Rupture22 (4%)45 (7%) ACLR18 (6%)34 (11%) ACLR + LET4 (1%)11 (4%)Overall Reoperation Rate54 (9%)102 (16%) ACLR32 (10%)56 (18%) ACLR + LET22 (7%)46 (15%) Open table in a new tab The authors would like to apologise for any inconvenience caused. No increase in adverse events with lateral extra-articular tenodesis augmentation of anterior cruciate ligament reconstruction – Results from the stability randomized trialJournal of ISAKOSVol. 8Issue 4PreviewResults from the Stability Study suggest that adding a lateral extra-articular tenodesis (LET) to a hamstring tendon autograft reduces the rate of anterior cruciate ligament reconstruction (ACLR) failure in high-risk patients. The purpose of this study is to report adverse events over the 2-year follow-up period and compare groups (ACLR alone vs. ACLR + LET). Full-Text PDF Open Access
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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 | 0.007 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.076 | 0.014 |
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".