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Improving the quality of evidence production in rehabilitation. Results of the 5th Cochrane Rehabilitation Methodological Meeting

2023· article· en· W4389947607 on OpenAlexaff
Stefano NEGRINI, Carlotte Kiekens, William Levack, Thorsten Meyer-Feil, Chiara Arienti, Pierre Côté

Bibliographic record

VenueEuropean Journal of Physical and Rehabilitation Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsMedicineRehabilitationPhysical therapyPhysical medicine and rehabilitationQuality (philosophy)

Abstract

fetched live from OpenAlex

The paper introduces the Special Sections of the European Journal of Physical and Rehabilitation Medicine dedicated to the 5th Methodological Meeting of Cochrane Rehabilitation. It introduces Cochrane Rehabilitation; its vision, mission and goals; discusses why the Methodological Meetings were created; and reports on their organisation and previous outcomes. The core content of this editorial is the 5th Methodological Meeting held in Milan in September 2023. The original title for this meeting was “The Rehabilitation Evidence Ecosystem: useful study designs.” The focus of the Milan meeting was informed by the lessons learned by Cochrane Rehabilitation in the past few years, by the new rehabilitation definition for research purposes, by the collaboration with the World Health Organization (WHO), and by the REH-COVER (Rehabilitation COVID-19 Evidence-Based Response) action. During the Meeting, participants discussed the current methodological evidence on the following: RCTs in rehabilitation coming from meta-epidemiological studies; observational study designs - specifically the IDEAL Framework (Idea, Development, Exploration, Assessment, Long-term study) and its potential implementation in rehabilitation and the Target Trial Emulation framework: Single Case Experimental Designs; complex intervention studies: health services research studies, and studies using qualitative approaches. The Meeting culminated in the development of a first version of a “road map” to navigate the evidence production in rehabilitation according to the previous discussions. The Special Sections’ papers present all topics discussed at the meeting, and a methodological paper about choosing the right research question, presenting final results and the “road map” for evidence production in rehabilitation.

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)0.019
metaresearch head score (Gemma)0.125
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.896
Threshold uncertainty score0.882

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0190.125
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.127
GPT teacher head0.405
Teacher spread0.278 · 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.

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".

Quick stats

Citations9
Published2023
Admission routes1
Has abstractyes

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