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Record W4401735949 · doi:10.1016/j.arthro.2024.07.039

High Variability in Standardized Outcome Thresholds of Clinically Important Changes in Shoulder Instability Surgery: A Systematic Review

2024· review· en· W4401735949 on OpenAlexaboutno aff
Ignacio Pasqualini, Luciano Andrés Rossi, Xuankang Pan, Patrick J. Denard, John P. Scanaliato, Jay M. Levin, Jonathan F. Dickens, Christopher S. Klifto, Eoghan T. Hurley

Bibliographic record

VenueArthroscopy The Journal of Arthroscopic and Related Surgery · 2024
Typereview
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsOutcome (game theory)MedicineInstabilitySystematic reviewSurgeryMEDLINEBiologyPhysicsMathematicsMechanics

Abstract

fetched live from OpenAlex

PURPOSE: To examine reported minimal clinically important difference (MCID) and patient-acceptable satisfactory state (PASS) values for patient-reported outcome measures (PROMs) after shoulder instability surgery and assess variability in published values depending on the surgery performed. Our secondary aims were to describe the methods used to derive MCID and PASS values in the published literature, including anchor-based, distribution-based, or other approaches, and to assess the frequency of MCID and PASS use in studies on shoulder instability surgery. METHODS: A systematic review of MCID and PASS values after Bankart, Latarjet, and Remplissage procedures was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA). The Embase, PubMed, and Cochrane Central Register of Controlled Trials (CENTRAL) databases were queried from 1985 to 2023. Inclusion criteria included studies written in English and studies reporting use of MCID or PASS for patient-reported outcome measures (PROMS) after Latarjet, Bankart, and Remplissage approaches for shoulder instability surgery. Extracted data included study population characteristics, intervention characteristics, and outcomes of interest. Continuous data were described using medians and ranges. Categorical variables, including PROMs and MCID/PASS methods, were described using percentages. Because MCID is a patient-level rather than a group-level metric, the authors confirmed that all included studies reported proportions (%) of subjects who met or exceeded the MCID. RESULTS: A total of 174 records were screened, and 8 studies were included in this review. MCID was the most widely used outcome threshold and was reported in all 8 studies, with only 2 studies reporting both the MCID and the PASS. The most widely studied PROMs were the American Shoulder and Elbow Surgeons (range 5.65-9.6 for distribution MCID, 8.5 anchor MCID, 86 anchor PASS); Single Assessment Numeric Evaluation (range 11.4-12.4 distribution MCID, 82.5-87.5 anchor PASS); visual analog scale (VAS) (range 1.1-1.7 distribution MCID, 1.5-2.5 PASS); Western Ontario Shoulder Instability Index (range 60.7-254.9 distribution MCID, 126.43 anchor MCID, 571-619.5 anchor PASS); and Rowe scores (range 5.6-8.4 distribution MCID, 9.7 anchor MCID). Notably, no studies reported on substantial clinical benefit or maximal outcome improvement. CONCLUSIONS: Despite the wide array of available PROMs for assessing shoulder instability surgery outcomes, the availability of clinically significant outcome thresholds such as MCID and PASS remains relatively limited. Although MCID has been the most frequently reported metric, there is considerable interstudy variability observed in their values. CLINICAL RELEVANCE: Knowing the outcome thresholds such as MCID and PASS of the PROMs frequently used to evaluate the results of glenohumeral stabilization surgery is fundamental because they allow us to know what is a clinically significant improvement for the patient.

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)0.052
metaresearch head score (Gemma)0.206
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.052
Threshold uncertainty score0.277

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0520.206
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0140.016
Bibliometrics0.0120.013
Science and technology studies0.0010.002
Scholarly communication0.0040.004
Open science0.0030.002
Research integrity0.0020.002
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 head0.057
GPT teacher head0.390
Teacher spread0.333 · 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 designSystematic review
Domainnot available
GenreReview

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
Published2024
Admission routes1
Has abstractyes

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