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Decision-Making and Decision Support in Acute Care

2013· book-chapter· en· W2494363424 on OpenAlexaff

Bibliographic record

VenueAdvances in healthcare information systems and administration book series · 2013
Typebook-chapter
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsDalhousie University
Fundersnot available
KeywordsDecision support systemClinical decision support systemCategorizationR-CASTClinical decision makingManagement scienceDecision analysisAffect (linguistics)Computer scienceBusiness decision mappingKnowledge managementMedicinePsychologyArtificial intelligenceFamily medicineEngineeringMathematics

Abstract

fetched live from OpenAlex

Clinical Decision Support Systems (CDSS) are information tools intended to optimize medical choice, promising better patient outcomes, faster care, reduced resource expenditure, or some combination of all three. Clinical trials of CDSS have provided only insipid evidence of benefit to date. This chapter reviews the theory of medical decision-decision making, identifying the different decision support needs of novices and experts, and demonstrates that discipline, objective and setting, and affect of the nature of support that is required. A discussion on categorization attempts to provide metrics by which systems can be compared and evaluated, in particular with regard to decision support mechanics and function. Throughout, the common theme is the placement of clinical decision makers at the center of the design or evaluation process.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.971
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.006
Open science0.0000.000
Research integrity0.0010.001
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 head0.022
GPT teacher head0.391
Teacher spread0.368 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2013
Admission routes1
Has abstractyes

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