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Record W1510747555 · doi:10.1136/jamia.2001.0080324

A Primer on Aspects of Cognition for Medical Informatics

2001· review· en· W1510747555 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.

Bibliographic record

VenueJournal of the American Medical Informatics Association · 2001
Typereview
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsMcGill University
FundersU.S. National Library of Medicine
KeywordsCognitionHealth informaticsMultidisciplinary approachVariety (cybernetics)InformaticsComputer scienceSet (abstract data type)Data scienceInformation scienceEngineering informaticsTranslational research informaticsCognitive scienceField (mathematics)PsychologyArtificial intelligenceMedicineEngineeringLibrary scienceNeuroscienceSociologySocial scienceNursing

Abstract

fetched live from OpenAlex

As a multidisciplinary field, medical informatics draws on a range of disciplines, such as computer science, information science, and the social and cognitive sciences. The cognitive sciences can provide important insights into the nature of the processes involved in human- computer interaction and help improve the design of medical information systems by providing insight into the roles that knowledge, memory, and strategies play in a variety of cognitive activities. In this paper, the authors survey literature on aspects of medical cognition and provide a set of claims that they consider to be important in medical informatics.

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)0.015
metaresearch head score (Gemma)0.024
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.927
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.066
GPT teacher head0.478
Teacher spread0.412 · 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