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Cochrane Rehabilitation: report of the first year of work

2018· article· en· W2895567497 on OpenAlexaff
Stefano Négrini, Chiara Arienti, Joel Pollet, Julia Patrick Engkasan, Francesca Gimigliano, Frane Grubišić, Tracey Howe, Elena Ilieva, William Levack, Antti Malmivaara, Thorsten Meyer, Aydan Oral, Farooq Azam Rathore, Carlotte Kiekens

Bibliographic record

VenueEuropean Journal of Physical and Rehabilitation Medicine · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicHealthcare Systems and Technology
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsRehabilitationWork (physics)Cochrane collaborationFistMedicineMedical educationPhysical therapySystematic reviewMEDLINEPolitical scienceEngineering

Abstract

fetched live from OpenAlex

Since his launch Cochrane Rehabilitation has started working to be a bridge between Cochrane and rehabilitation. After a fist period of work organization, the field has started producing actions through its committees: communication, education, methodology, publication and reviews. All the results of this first year of activity are listed in this report.

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)0.040
metaresearch head score (Gemma)0.127
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.960
Threshold uncertainty score0.213

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0400.127
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0390.016
Science and technology studies0.0040.002
Scholarly communication0.0150.005
Open science0.0040.010
Research integrity0.0060.006
Insufficient payload (model declined to judge)0.0250.017

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 head0.010
GPT teacher head0.258
Teacher spread0.248 · 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 designNot applicable
DomainMethods
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

Citations4
Published2018
Admission routes1
Has abstractyes

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