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Record W4415532133 · doi:10.1016/j.jcm.2025.09.024

Disability and Functionality Measures in Patients With Acute Ankle Sprain: A Scoping Review

2025· review· en· W4415532133 on OpenAlexaff
Andressa de Souza, Carolina Santarelli, Luiza Murakami, Neidi Celena Cortez Limachi, Verônica Souza Santos

Bibliographic record

VenueJournal of Chiropractic Medicine · 2025
Typereview
Languageen
FieldMedicine
TopicFoot and Ankle Surgery
Canadian institutionsMcMaster University
Fundersnot available
KeywordsRandomized controlled trialInternational Classification of Functioning, Disability and HealthData extractionAnklePromAnkle sprainMEDLINE

Abstract

fetched live from OpenAlex

Objective: The purpose of this review was to identify how disability and functionality were measured in patients with acute ankle sprain and present according to the International Classification of Function, Disability, and Health (ICF). Methods: We conducted a scoping review of randomized controlled trials including participants with acute ankle sprain. We conducted the searches in 4 databases to identify the studies. The searchers considered the inception of databases up to February 2024, without language restrictions. The process of evidence selection and data extraction was conducted independently. The summarization of evidence was presented according to the ICF. Results: We included 49 randomized controlled trials. The most common way disability and functionality were assessed in the included studies was through patient-reported outcome measures (PROMs) 35 (71.4%), and the most used PROM was the Foot and Ankle Outcome Score (FAOS). Most of the tools used to measure disability and functionality fall under the participation domain of the International Classification of Functioning, followed by activity. Conclusion: Disability and functionality in randomized controlled trials of acute ankle sprain is usually assessed through PROMs. The most comprehensive item of the ICF was participation, followed by activity.

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)0.003
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.703
Threshold uncertainty score0.945

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.068
GPT teacher head0.390
Teacher spread0.322 · 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 teacher head, 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

Citations0
Published2025
Admission routes1
Has abstractyes

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