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Record W113870509 · doi:10.1055/s-0038-1634293

Analysis of the Process of Encoding Guidelines: A Comparison of GLIF2 and GLIF3

2002· article· en· W113870509 on OpenAlexfundno aff
T. Branch, Di Wang, Mor Peleg, Aziz A. Boxwala, V. L. Patel

Bibliographic record

VenueMethods of Information in Medicine · 2002
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBiomedical Text Mining and Ontologies
Canadian institutionsnot available
FundersMedical Research CouncilU.S. National Library of MedicineMedical Research Council Canada
KeywordsFormalityEncoding (memory)GuidelineAmbiguityClinical PracticeComputer scienceProcess (computing)Knowledge translationNatural language processingInformation retrievalMedicineKnowledge managementArtificial intelligenceLinguisticsFamily medicinePathologyProgramming language

Abstract

fetched live from OpenAlex

OBJECTIVES: This study aims to evaluate the use of a modified version of the Guideline Interchange Format (GLIF), GLIF3, in the translation of clinical practice guidelines into an electronically encoded form such that they may be shared among various clinical institutions and settings. METHODS: Based on theories and methods from cognitive science, the encoding of two clinical practice guidelines into two guideline modeling methods (GLIF3 and an earlier version, GLIF2) by two medical informaticians was captured on video and transcribed and annotated for analysis. RESULTS: Differing in both content and structure, the representations developed in GLIF3 were found to contain a greater level of representational detail and less ambiguity than those developed in GLIF2. CONCLUSIONS: The use of GLIF3 in the encoding of clinical guidelines offers significant improvements due to its greater formality as compared to earlier versions of GLIF.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.639
Threshold uncertainty score0.369

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.080
GPT teacher head0.453
Teacher spread0.373 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations22
Published2002
Admission routes1
Has abstractyes

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