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Mind the gap: observation windows to define periods of event ascertainment as a quality control method for longitudinal electronic health record data

2019· article· en· W2918292484 on OpenAlexafffund
Keri N. Althoff, Cherise Wong, Brenna Hogan, Fidel Desir, Bin You, Elizabeth Humes, Jinbing Zhang, Yuezhou Jing, Sharada P. Modur, Jennifer Lee, Aimee Freeman, Mari M. Kitahata, Stephen Van Rompaey, William C. Mathews, Michael A. Horberg, Michael J. Silverberg, Ángel M. Mayor, Kate Salters, Richard D. Moore, Stephen J. Gange

Bibliographic record

VenueAnnals of Epidemiology · 2019
Typearticle
Languageen
FieldMedicine
TopicDiabetes, Cardiovascular Risks, and Lipoproteins
Canadian institutionsAIDS Vancouver
FundersNational Institute of Allergy and Infectious DiseasesNational Institute of Mental HealthNational Heart, Lung, and Blood InstituteNational Eye InstituteNational Institute on AgingNational Cancer InstituteNational Institute on Alcohol Abuse and AlcoholismCanadian Institutes of Health ResearchNational Institutes of HealthEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentGovernment of AlbertaAgency for Healthcare Research and QualityNational Center for Advancing Translational SciencesHealth Resources and Services AdministrationNational Institute on Drug AbuseNational Institute on Minority Health and Health DisparitiesNational Center for Research ResourcesCenters for Disease Control and Prevention
KeywordsMedicineQuality (philosophy)Event (particle physics)Control (management)Health recordsEvent dataElectronic health recordData qualityLongitudinal dataStatisticsData miningArtificial intelligenceOperations managementHealth care

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.162
metaresearch head score (Gemma)0.358
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.838
Threshold uncertainty score0.854

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1620.358
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.003
Science and technology studies0.0010.002
Scholarly communication0.0030.006
Open science0.0030.005
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0030.000

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 head0.290
GPT teacher head0.495
Teacher spread0.205 · 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 designSimulation or modeling
DomainMethods
GenreMethods

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

Citations24
Published2019
Admission routes2
Has abstractno

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