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Record W7083489991 · doi:10.1177/2325967125s00147

Poster 36: Defining the Minimal Clinically Important Difference and Patient Acceptable Symptom State for Arthroscopic Anterior Shoulder Stabilization at 2 Year Follow-Up

2025· article· en· W7083489991 on OpenAlexaboutno aff

Bibliographic record

VenueOrthopaedic Journal of Sports Medicine · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsnot available
Fundersnot available
KeywordsMinimal clinically important differenceOrthopedic surgeryArthroscopyLogistic regressionAnterior shoulderRetrospective cohort studyTearsSeverity of illness

Abstract

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Objectives: While functional outcomes and failure rates have been extensively studied, there is a paucity of literature describing the clinically significant outcomes of patients who have successfully undergone arthroscopic stabilization and do not have a subsequent dislocation. The minimum clinically important difference (MCID) was developed to determine the smallest change in a patient’s condition that is clinically significant following an orthopedic surgery procedure. Similarly, the patient acceptable symptom state (PASS) denotes the postoperative level of symptoms and functional state that patients deems to be satisfacotry.The aim of this study is to establish the MCID and PASS thresholds for arthroscopic anterior shoulder stabilization at a minimum two-year follow-up. Factors that are predictive of MCID and PASS achievement, including preoperative, demographic, and intraoperative variables were evaluated in this study. The authors hypothesized that symptom duration and recurrent frank dislocations prior to stabilization would be predictive of MCID and PASS achievement. Methods: Patients who underwent primary arthroscopic stabilization for anterior-inferior labral tears from March 2018 to December 2021 with a minimum of 2 years follow-up were retrospectively identified through a prospectively-maintained institutional database. MCID thresholds were determined by a distribution-based method whereas PASS thresholds were established using an anchor-based method. The PROMs analyzed included Western Ontario Shoulder Instability Index (WOSI) score, Single Assessment Numeric Evaluation (SANE) score, Patient-Reported Outcomes Measurement Information System Upper Extremity (PROMIS UE) score and Veterans Rand (VR) 12 score. Multivariate logistic regressions were performed to determine factors associated with MCID and PASS achievement. Results: The demographic and intraoperative characteristics of the sixty-five included patients are described in Table 1 and Table 2 , respectively. The thresholds for MCID achievement and associated achievement rates were as follows: WOSI, 12.8 (84.6%); SANE, 14.4 (81.5%); PROMIS UE 4.4 (90.8%); VR 12 Physical, 3.9 (76.9%). The thresholds for PASS achievement and associated achievement rates were as follows: WOSI, 40.2 (80.0%); SANE, 74.9 (86.2%); PROMIS UE 40.5 (84.8%); VR 12 Physical, 49.9 (83.1%). Symptom duration > 6 months was predictive of failing to achieve MCID for WOSI and PASS for WOSI and SANE. Preoperative hyperlaxity was predictive of failing to achieve PASS for WOSI and PROMIS UE. A lower BMI was predictive of achieving PASS for WOSI, PROMIS UE and VR 12. The presence of an ALPSA lesion was predictive of failing to achieve PASS for WOSI and failing to achieve MCID and PASS for WOSI, PROMIS UE and VR12 simultaneously. Table 3 displays MCID and PASS thresholds and percentage of achievement among included study patients. Figure 1 displays the receiver operating curves for PASS thresholds. Conclusions: This study defines the thresholds for MCID and PASS achievement at minimum 2 year follow-up in a cohort of patients undergoing arthroscopic anterior shoulder stabilization. Factors such as symptom duration, hyperlaxity, BMI and ALPSA lesions were found to be predictive of MCID and PASS achievement at short-term follow-up.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.082
GPT teacher head0.377
Teacher spread0.295 · 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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