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Record W4250154871 · doi:10.31219/osf.io/pnv25

A scoping review of the predictive models of diabetes complications

2021· review· en· W4250154871 on OpenAlexaff
Ruth Ndjaboué, Gérard Ngueta, Charlotte Rochefort-Brihay, Sasha Delorme, Daniel Guay, Noah Ivers, Baiju R. Shah, Sharon E. Straus, Catherine Yu, Sandrine Comeau, Imen Farhat, Charles H. Racine, Olivia Drescher, Holly O. Witteman

Bibliographic record

Venuenot available
Typereview
Languageen
FieldMedicine
TopicDiabetes, Cardiovascular Risks, and Lipoproteins
Canadian institutionsMcGill UniversityUniversité LavalSt. Michael's HospitalCanada Research ChairsUniversity of TorontoUniversity of New BrunswickHealth Sciences CentreSunnybrook Health Science CentreWomen's College Hospital
Fundersnot available
KeywordsDiabetes mellitusMedicineIntensive care medicinePredictive modellingComputer scienceMachine learningEndocrinology

Abstract

fetched live from OpenAlex

Description of prognostic prediction models of diabetes-related complications published between 2000 and April 2020

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)0.012
metaresearch head score (Gemma)0.040
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.013
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.040
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0070.007
Bibliometrics0.0110.015
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0030.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.073
GPT teacher head0.349
Teacher spread0.276 · 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.

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

Citations2
Published2021
Admission routes1
Has abstractyes

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