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Record W2735730740 · doi:10.1177/0363546517712952

Surgical Predictors of Clinical Outcomes After Revision Anterior Cruciate Ligament Reconstruction

2017· article· en· W2735730740 on OpenAlexaboutno aff
Christina R. Allen, Allen F. Anderson, Daniel E. Cooper, Thomas M. DeBerardino, Warren R. Dunn, Amanda K. Haas, Laura J. Huston, Brett A. Lantz, Barton J. Mann, Sam Nwosu, Kurt P. Spindler, Michael J. Stuart, Rick W. Wright, John P. Albright, Annunziato Amendola, Jack T. Andrish, Christopher C. Annunziata, Robert A. Arciero, Bernard R. Bach, Champ L. Baker, Arthur R. Bartolozzi, Keith M. Baumgarten, Jeffery R. Bechler, Jeffrey H. Berg, Geoffrey A. Bernas, Stephen F. Brockmeier, Robert H. Brophy, Charles A. Bush‐Joseph, J. Brad Butler, John Campbell, James L. Carey, James E. Carpenter, Brian J. Cole, Jonathan M. Cooper, Charles L. Cox, R. Alexander Creighton, Diane L. Dahm, Tal S. David, David C. Flanigan, Robert W. Frederick, Theodore J. Ganley, Elizabeth A. Garofoli, Charles J. Gatt, Steven R. Gecha, J. Robert Giffin, Sharon L. Hame, Jo A. Hannafin, Christopher D. Harner, Norman Lindsay Harris, Keith S. Hechtman, Elliott B. Hershman, Rudolf G. Hoellrich, Timothy M. Hosea, David C. Johnson, Timothy S. Johnson, Morgan H. Jones, Christopher C. Kaeding, Ganesh V. Kamath, Thomas E. Klootwyk, Bruce A. Levy, C. Benjamin, G. Peter Maiers, Robert G. Marx, Matthew J. Matava, Gregory M. Mathien, David R. McAllister, Eric C. McCarty, Robert G. McCormack, Bruce S. Miller, Carl W. Nissen, Daniel F. O’Neill, Brett D. Owens, Richard D. Parker, Mark L. Purnell, Arun J. Ramappa, Michael A. Rauh, Arthur C. Rettig, Jon K. Sekiya, Kevin G. Shea, Orrin H. Sherman, James R. Slauterbeck, Matthew V. Smith, Jeffrey T. Spang, Steven J. Svoboda, Timothy N. Taft, Joachim J. Tenuta, Edwin M. Tingstad, Armando F. Vidal, Darius G. Viskontas, Richard A. White, James S. Williams, Michelle L. Wolcott, Brian R. Wolf, James J. York

Bibliographic record

VenueThe American Journal of Sports Medicine · 2017
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsnot available
FundersNational Institute of Arthritis and Musculoskeletal and Skin Diseases
KeywordsMedicineAnterior cruciate ligamentBody mass indexAnterior cruciate ligament reconstructionOsteoarthritisSurgeryWOMACArthrotomyACL injuryEvidence-based medicineOrthopedic surgeryPhysical therapyArthroscopyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Revision anterior cruciate ligament (ACL) reconstruction has been documented to have worse outcomes compared with primary ACL reconstruction. HYPOTHESIS: Certain factors under the control of the surgeon at the time of revision surgery can both negatively and positively affect outcomes. STUDY DESIGN: Case-control study; Level of evidence, 3. METHODS: Patients undergoing revision ACL reconstruction were identified and prospectively enrolled between 2006 and 2011. Data collected included baseline demographics, intraoperative surgical technique and joint disorders, and a series of validated patient-reported outcome instruments (International Knee Documentation Committee [IKDC] subjective form, Knee Injury and Osteoarthritis Outcome Score [KOOS], Western Ontario and McMaster Universities Osteoarthritis Index [WOMAC], and Marx activity rating scale) completed before surgery. Patients were followed up for 2 years and asked to complete an identical set of outcome instruments. Regression analysis was used to control for age, sex, body mass index (BMI), activity level, baseline outcome scores, revision number, time since last ACL reconstruction, and a variety of previous and current surgical variables to assess the surgical risk factors for clinical outcomes 2 years after revision ACL reconstruction. RESULTS: A total of 1205 patients (697 male [58%]) met the inclusion criteria and were successfully enrolled. The median age was 26 years, and the median time since their last ACL reconstruction was 3.4 years. Two-year follow-up was obtained on 82% (989/1205). Both previous and current surgical factors were found to be significant contributors toward poorer clinical outcomes at 2 years. Having undergone previous arthrotomy (nonarthroscopic open approach) for ACL reconstruction compared with the 1-incision technique resulted in significantly poorer outcomes for the 2-year IKDC ( P = .037; odds ratio [OR], 2.43; 95% CI, 1.05-5.88) and KOOS pain, sports/recreation, and quality of life (QOL) subscales ( P ≤ .05; OR range, 2.38-4.35; 95% CI, 1.03-10.00). The use of a metal interference screw for current femoral fixation resulted in significantly better outcomes for the 2-year KOOS symptoms, pain, and QOL subscales ( P ≤ .05; OR range, 1.70-1.96; 95% CI, 1.00-3.33) as well as WOMAC stiffness subscale ( P = .041; OR, 1.75; 95% CI, 1.02-3.03). Not performing notchplasty at revision significantly improved 2-year outcomes for the IKDC ( P = .013; OR, 1.47; 95% CI, 1.08-1.99), KOOS activities of daily living (ADL) and QOL subscales ( P ≤ .04; OR range, 1.40-1.41; 95% CI, 1.03-1.93), and WOMAC stiffness and ADL subscales ( P ≤ .04; OR range, 1.41-1.49; 95% CI, 1.03-2.05). Factors before revision ACL reconstruction that increased the risk of poorer clinical outcomes at 2 years included lower baseline outcome scores, a lower Marx activity score at the time of revision, a higher BMI, female sex, and a shorter time since the patient's last ACL reconstruction. Prior femoral fixation, prior femoral tunnel aperture position, and knee flexion angle at the time of revision graft fixation were not found to affect 2-year outcomes in this revision cohort. CONCLUSION: There are certain surgical variables that the physician can control at the time of revision ACL reconstruction that can modify clinical outcomes at 2 years. Whenever possible, opting for an anteromedial portal or transtibial surgical exposure, choosing a metal interference screw for femoral fixation, and not performing notchplasty are associated with significantly better 2-year clinical outcomes.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.523
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.363
Teacher spread0.345 · 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 teacher head, not a consensus.

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".

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Citations37
Published2017
Admission routes1
Has abstractyes

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