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Record W4239968815 · doi:10.1177/2325967114s00066

Meniscal Repair with Concurrent Anterior Cruciate Ligament Reconstruction: Operative Success and Patient Outcomes at 6-Year Follow-up

2014· article· en· W4239968815 on OpenAlexaboutno aff
Robert W. Westermann, Rick W. Wright, Laura J. Huston

Bibliographic record

VenueOrthopaedic Journal of Sports Medicine · 2014
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMeniscusAnterior cruciate ligament reconstructionSurgeryOsteoarthritisWOMACAnterior cruciate ligamentMedial meniscusLateral meniscusOrthopedic surgery

Abstract

fetched live from OpenAlex

Objectives: Meniscus repairs are commonly performed concurrently with anterior cruciate ligament reconstruction (ACLR) in the acutely injured knee. Properly functioning menisci coupled with knee stability are thought to be critical factors in achieving optimal outcomes. While meniscal repair in conjunction with ACLR has demonstrated good success at 2 years, no large-scale, prospective, multicenter studies have evaluated long-term patient-oriented outcomes after combined ACLR and meniscus repair. We hypothesize that patient-centered outcome scores will deteriorate and ipsilateral reoperations will increase at 6 years following combined ACLR and meniscus repair. Methods: All unilateral primary ACL reconstructions from the Multicenter Orthopaedic Outcomes Network (MOON) between 2002 and 2004 were evaluated, and patients who underwent concurrent meniscus repair were selected. Validated patient-oriented outcome data [Knee Injury and Osteoarthritis Outcome Score (KOOS), Western Ontario and McMaster Universities (WOMAC) scores, Marx activity scores and International Knee Documentation Committee (IKDC) scores] was gathered at 2 and 6 years following the index procedure. Subsequent ipsilateral knee re-operation was confirmed by operative reports to evaluate for failure of meniscal repairs. Results: In total, 1440 primary ACLR’s were performed between 2002 and 2004 as part of the study cohort. Of these, 286 subjects underwent concurrent meniscus repair (298 meniscal repairs). 235/286 (82.2%) were available for follow up at 6 years (154 medial meniscus repairs, 72 lateral meniscal repairs, and 9 patients who underwent both lateral and medial meniscal repairs). Overall, the success rate of meniscal repair at the time of ACLR was 86% (202/235) at 6 years. We found an 86.4% six year success rate with combined ACLR and medial meniscal repair, 86.1% for lateral meniscal repairs and 77.8% when both medial and lateral menisci were repaired. 27.3% (9/33) of the failures were associated revision ACL surgery. Medial meniscal repairs failed earlier (mean 2.1 years) than lateral meniscal repairs (mean 3.7 years) (p=0.01). All-inside techniques were performed in 88.5% of cases. There were 31 failures with this technique representing a 14.9% failure rate. There was one failure in the inside-out technique group (1/19, 5.2%), and one failure noted in the outside-in technique group (1/6, 16.6%). Significant improvements were observed in patient reported outcomes [KOOS Symptoms, KOOS Pain, KOOS KRQOL, WOMAC Pain, and IKDC scores] when baseline scores were compared to 6-year follow-up. No significant clinical differences were observed between 2 and 6 year follow up indicating there was no clinical deterioration over this time period. Marx Activity levels gradually declined from time of injury to 6-year follow-up. Conclusion: Concurrent meniscal repair with ACLR is associated with success rates approximating 86% at 6-year follow-up. Patient-oriented outcome measures were generally similar between 2 and 6 years follow up. Surgeons may expect good clinical outcomes 6 years after combined ACLR and meniscus repairs.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.007
GPT teacher head0.262
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2014
Admission routes1
Has abstractyes

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