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Record W2748018347 · doi:10.1503/cmaj.170807

Routinely collected data: the importance of high-quality diagnostic coding to research

2017· letter· en· W2748018347 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueCanadian Medical Association Journal · 2017
Typeletter
Languageen
FieldHealth Professions
TopicMedical Coding and Health Information
Canadian institutionsOttawa Public HealthChildren's Hospital of Eastern OntarioUniversity of Ottawa
FundersNational Institute for Health and Care ResearchWellcome Trust
KeywordsData scienceComputer scienceCoding (social sciences)Data collectionKey (lock)Data qualityHealth recordsInformation retrievalData miningHealth careStatistics

Abstract

fetched live from OpenAlex

See related article at [www.cmajopen.ca/content/5/3/E617][1] KEY POINTS Routinely collected health data are data collected for purposes other than research or without specific a priori research questions developed before collection.[1][2] Examples include clinical information from electronic health

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.

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)0.047
metaresearch head score (Gemma)0.268
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.220
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0470.268
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0050.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0030.019
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 head0.418
GPT teacher head0.530
Teacher spread0.112 · 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