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Cochrane Rehabilitation: 2019 annual report

2020· article· en· W3008101671 on OpenAlexaff
Chiara Arienti, Carlotte Kiekens, Roberta Bettinsoli, Julia Patrick Engkasan, Francesca Gimigliano, Frane Grubišić, Tracey Howe, Elena Ilieva, Stefano Giuseppe Lazzarini, William Levack, Antti Malmivaara, Thorsten Meyer, Aydan Oral, Michele Patrini, Joel Pollet, Farooq Azam Rathore, Stefano Négrini

Bibliographic record

VenueEuropean Journal of Physical and Rehabilitation Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsRehabilitationCochrane LibraryMedicineChecklistSystematic reviewMEDLINEKnowledge translationMedical educationMeta-analysisPhysical therapyFamily medicinePsychologyPolitical scienceKnowledge managementPathology

Abstract

fetched live from OpenAlex

During its third year of existence, Cochrane Rehabilitation goals included to point out the main methodological issues in rehabilitation research, and to increase the Knowledge Translation activities. This has been performed through its committees and specific projects. In 2019, Cochrane Rehabilitation worked on five different special projects at different stages of development: 1) a collaboration with the World Health Organization to extract the best evidence for Rehabilitation (Be4rehab); 2) the development of a reporting checklist for Randomised Controlled Trials in rehabilitation (RCTRACK); 3) the definition of what is the rehabilitation for research purposes; 4) the ebook project; and 5) a prioritization exercise for Cochrane Reviews production. The Review Committee finalized the screening and "tagging" of all rehabilitation reviews in the Cochrane library; the Publication Committee increased the number of international journals with which publish Cochrane Corners; the Education Committee continued performing educational activities such as workshops in different meetings; the Methodology Committee performed the second Cochrane Rehabilitation Methodological Meeting and published many papers; the Communication Committee spread the rehabilitation evidence through different channels and translated the contents in different languages. The collaboration with several National and International Rehabilitation Scientific Societies, Universities, Hospitals, Research Centers and other organizations keeps on growing.

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)0.002
metaresearch head score (Gemma)0.012
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 score0.748
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.027
GPT teacher head0.340
Teacher spread0.313 · 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

Citations10
Published2020
Admission routes1
Has abstractyes

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