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Record W3084021917 · doi:10.1136/rapm-2020-101670

Is the minimal clinically important difference (MCID) in acute pain a good measure of analgesic efficacy in regional anesthesia?

2020· review· en· W3084021917 on OpenAlexaff
Felipe Muñoz-Leyva, Kariem El‐Boghdadly, Vincent Chan

Bibliographic record

VenueRegional Anesthesia & Pain Medicine · 2020
Typereview
Languageen
FieldMedicine
TopicAnesthesia and Pain Management
Canadian institutionsUniversity of TorontoToronto Western HospitalUniversity Health Network
Fundersnot available
KeywordsMinimal clinically important differenceMedicineAnalgesicAcute painPhysical therapyPostoperative painIntensive care medicineAnesthesiaRandomized controlled trialSurgery

Abstract

fetched live from OpenAlex

In the field of acute pain medicine research, we believe there is an unmet need to incorporate patient related outcome measures that move beyond reporting pain scores and opioid consumption. The term "minimal clinically important difference" (MCID) defines the clinical benefit of an intervention as perceived by the patient, as opposed to a mathematically determined statistically significant difference that may not necessarily be clinically significant. The present article reviews the concept of MCID in acute postoperative pain research, addresses potential pitfalls in MCID determination and questions the clinical validity of extrapolating MCID determined from chronic pain and non-surgical pain studies to the acute postoperative pain setting. We further suggest the concepts of minimal clinically important improvement, substantial clinical benefit and patient acceptable symptom state should also represent aspirational outcomes for future research in acute postoperative pain management.

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)0.017
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.765
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.001
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0060.002
Bibliometrics0.0010.003
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0010.002
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 head0.070
GPT teacher head0.333
Teacher spread0.263 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations93
Published2020
Admission routes1
Has abstractyes

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