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Record W2979479033 · doi:10.2106/jbjs.rvw.17.00159

Minimum Clinically Important Difference: Current Trends in the Orthopaedic Literature, Part I: Upper Extremity

2018· review· en· W2979479033 on OpenAlexaboutno aff
Anne G. Copay, Andrew S. Chung, Blake Eyberg, Neil Olmscheid, Norman Chutkan, Mark J. Spangehl

Bibliographic record

VenueJBJS Reviews · 2018
Typereview
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMinimal clinically important differenceMedicineOrthopedic surgeryPhysical therapySports medicineMEDLINEMedical physicsPhysical medicine and rehabilitationSurgeryRandomized controlled trial

Abstract

fetched live from OpenAlex

BACKGROUND: The minimum clinically important difference (MCID) attempts to define the patient's experience of treatment outcomes. Efforts at calculating the MCID have yielded multiple and inconsistent MCID values. The purposes of this review were to describe the usage of the MCID in the most recent orthopaedic literature, to explain the limitations of its current uses, and to clarify the underpinnings of the MCID calculation, so as to help practitioners to understand and utilize the MCID and to guide future efforts to calculate the MCID. In Part I of this review, we sampled the orthopaedic literature in relation to the upper extremity. In this part, Part II, of the review, we will focus on the lower-extremity literature. METHODS: A review was conducted of the 2014 to 2016 MCID-related publications in The Journal of Arthroplasty, The Journal of Bone & Joint Surgery, The American Journal of Sports Medicine, Foot & Ankle International, Journal of Orthopaedic Trauma, Journal of Pediatric Orthopaedics, and Journal of Shoulder and Elbow Surgery. Only clinical science articles utilizing patient-reported outcome measures (PROMs) were included in the analysis. A keyword search was then performed to identify articles that used the MCID. Articles were then further categorized into upper-extremity and lower-extremity publications. The MCID utilization in the selected articles was characterized and was recorded. RESULTS: The MCID was referenced in 129 (7.5%) of 1,709 clinical science articles that utilized PROMs: 79 (61.2%) of the 129 articles were related to the lower extremity; of these, 11 (13.9%) independently calculated the MCID values and 68 (86.1%) used previously published MCID values as a gauge of their own results. The MCID values were calculated or were considered for 31 PROMs, of which 24 were specific to the lower extremity. Eleven different methods were used to calculate the MCID. The MCID had a wide range of values for the same questionnaires, for instance, 5.8 to 31.3 points for the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). CONCLUSIONS: There are more than twice as many PROMs for the lower extremity (24) than for the upper extremity (11), confirming that the determination of useful MCID values is, in part, hampered by the proliferation of PROMs in the field of orthopaedics. The difference between significance and clinical importance needs to be further clarified. For instance, the common use of determining sample size with the MCID and comparing group means with the MCID implies that a significant result will also be clinically important. Further, the study of the MCID would benefit from consensus agreement on relevant terminology and the appropriate usage of the MCID determining equations. CLINICAL RELEVANCE: MCID is increasingly used as a measure of patients' improvement. However, MCID does not yet adequately capture the clinical importance of patients' improvement.

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)0.023
metaresearch head score (Gemma)0.108
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.977
Threshold uncertainty score0.122

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.108
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0380.039
Science and technology studies0.0010.003
Scholarly communication0.0060.008
Open science0.0020.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.100
GPT teacher head0.415
Teacher spread0.315 · 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.

Study designSystematic review
DomainMethods
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

Citations154
Published2018
Admission routes1
Has abstractyes

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