MétaCan
Menu
Back to cohort

Scripts and Medical Diagnostic Knowledge

2000· article· en· W2078213917 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

VenueAcademic Medicine · 2000
Typearticle
Languageen
FieldMedicine
TopicClinical Reasoning and Diagnostic Skills
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsScripting languageCategorizationComputer scienceTask (project management)CognitionCompetence (human resources)Medical knowledgeArtificial intelligenceNatural language processingPsychologyMedical educationMedicineSocial psychologyProgramming language

Abstract

fetched live from OpenAlex

Medical diagnosis is a categorization task that allows physicians to make predictions about features of clinical situations and to determine appropriate course of action. The script concept, which first arose in cognitive psychology, provides a theoretical framework to explain how medical diagnostic knowledge can be structured for diagnostic problem solving. The main characteristics of the script concept are pre-stored knowledge, values acceptable or not acceptable for each illness attribute, and default values. Scripts are networks of knowledge adapted to goals of clinical tasks. The authors describe how scripts are used in diagnostic tasks, how the script concept fits within the clinical reasoning literature, how it contrasts with competing theories of clinical reasoning, how educators can help students build and refine scripts, and how scripts can be used to assess clinical competence.

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)0.001
metaresearch head score (Gemma)0.162
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.601
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.162
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0130.001

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 head0.034
GPT teacher head0.380
Teacher spread0.345 · 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