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Record W2894952064 · doi:10.1177/0363546518796838

Acetabular Labral Reconstruction: Development of a Tool to Predict Outcomes

2018· article· en· W2894952064 on OpenAlexaboutno aff
George F. Lebus, Karen K. Briggs, Grant J. Dornan, Shannen McNamara, Marc J. Philippon

Bibliographic record

VenueThe American Journal of Sports Medicine · 2018
Typearticle
Languageen
FieldMedicine
TopicHip disorders and treatments
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceOrthodonticsMedicine

Abstract

fetched live from OpenAlex

BACKGROUND: Acetabular labral reconstruction has demonstrated good results for labral lesions not amenable to labral repair. PURPOSE: To determine the predictors of outcomes at a minimum 2 years after labral reconstruction. STUDY DESIGN: Case series; Level of evidence, 4. METHODS: Patients included in the study underwent labral reconstruction with a minimum 2-year follow-up. The primary outcome variable was the Hip Outcome Score-Activities of Daily Living (HOS-ADL). Secondary outcome measures included the 12-item Short Form Health Survey physical component summary (SF-12 PCS) and patient satisfaction with surgical outcomes. Preoperative and intraoperative variables assessed included demographics, prior surgery, chronicity of symptoms, radiographic measurements, preoperative outcome scores, and findings at arthroscopic surgery. Predictors were assessed using logistic regression with restricted cubic splines. Bivariate statistics assessed risk factors for reoperation including revision arthroscopic surgery and total hip arthroplasty (THA). RESULTS: Three hundred seventeen of 368 labral reconstructions were available for follow-up (86.1%). Of these, 42 were converted to THA (13.2%) and 35 required revision arthroscopic surgery after labral reconstruction (11.0%). Factors associated with THA included older age, ≥2 previous surgeries, ≤2 mm of joint space, and lateral center edge angle (LCEA) <25°. Factors associated with revision included female sex, ≥2 previous surgeries, and LCEA <25°. Six patients refused to participate (1.9%), leaving 234 with a minimum follow-up of 2 years (mean, 3.7 years [range, 2.0-11.3 years]). These patients had significant improvement in HOS-ADL (71 to 90; P < .001), HOS-Sport (47 to 75; P < .001), Western Ontario and McMaster Universities Osteoarthritis Index (27 to 9; P < .001), modified Harris Hip Score (65 to 85; P < .001), and SF-12 PCS scores (41.6 to 53.1; P < .001). Median postoperative satisfaction was 9. Predictors of improvement for the HOS-ADL included higher preoperative HOS-ADL scores ( P < .001), joint space >2 mm ( P = .004), and no prior surgery ( P = .039). Predictors of improvement for the SF-12 PCS included higher preoperative SF-12 PCS scores ( P < .001), subacute chronicity (3 months to 1 year) of symptoms ( P = .013), and joint space >2 mm ( P = .046). Joint space >2 mm ( P < .001) and higher preoperative SF-12 scores (PCS: P = .034; mental component summary: P = .039) predicted higher satisfaction. CONCLUSION: At a minimum 2 years' follow-up, patients who did not undergo conversion to THA (13.2%) or require revision (11.0%), reported significant improvement in outcome scores and high satisfaction with outcomes. Predictors of revision or THA included ≥2 previous surgeries, low LCEA, female sex for revision, and narrowed joint space for THA. Higher preoperative outcome scores were the most significant predictors of improvement after labral reconstruction. Lower preoperative scores, joint space narrowing, and history of surgery were predictive of an inferior result and decreased postoperative satisfaction.

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)0.008
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.028
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.003
Science and technology studies0.0000.000
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.013
GPT teacher head0.283
Teacher spread0.270 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations29
Published2018
Admission routes1
Has abstractyes

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