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Record W2319232971 · doi:10.1017/s0317167100017170

Neurological Registry Quality Control and Quality Assurance

2013· review· en· W2319232971 on OpenAlexaffvenue
James Marriott, Vanessa K. Noonan, Elizabeth Donner, Mark Lowerison, Darren Lam, Lundy Day, Janet Warner, Eric E. Smith, Jean K. Mah, Paula de Robles, Nathalie Jetté, Megan Johnston, Tamara Pringsheim, Lawrence Korngut

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2013
Typereview
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsHotchkiss Brain InstituteUniversity of CalgaryHospital for Sick ChildrenUniversity of TorontoSickKids FoundationPraxis Spinal Cord InstituteUniversity of Manitoba
Fundersnot available
KeywordsQuality assuranceQuality (philosophy)Control (management)MedicineAction (physics)Quality controlComputer scienceMedical physicsArtificial intelligencePathology

Abstract

fetched live from OpenAlex

This section of the guideline discusses procedures and best practices around quality control and quality assurance. In developing this section of the guideline we reviewed available literature and best practice; consulted with registry and disease experts; and derived consensus recommendations.

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)0.049
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.679
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0190.049
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0020.010
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0010.004
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 head0.382
GPT teacher head0.500
Teacher spread0.118 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations3
Published2013
Admission routes2
Has abstractyes

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