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Record W2163752144

Effects of Computerized Clinical Decision Support Systems on Practitioner Performance and Patient Outcomes

2007· article· en· W2163752144 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

Venuenot available
Typearticle
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsMcMaster University
Fundersnot available
KeywordsClinical decision support systemPagerChartMedicineMedical emergencyMedical recordDecision support systemElectronic medical recordHealth careClinical decision makingMedical physicsComputer scienceFamily medicineArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

COMPUTERIZED CLINICAL DECIs ion suppor t sys t ems (CDSSs) are information systems designed to improve clinical decision making. Characteristics of individual patients are matched to a computerized knowledge base, and software algorithms generate patientspecific recommendations. Practitioners, health care staff, or patients can manually enter patient characteristics into the computer system; alternatively, electronic medical records can be queried for retrieval of patient characteristics. Computer-generated recommendations are delivered to the clinician through the electronic medical record, by pager, or through printouts placed in a patient’s paper chart. Such systems have been developed for a myriad of clinical issues, including diagnosis of chest pain, treatment of infertility, and timely administration of immunizations. These systems provide several modes of decision support, including alerts of critical val-

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.170
Threshold uncertainty score0.463

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.048
GPT teacher head0.464
Teacher spread0.417 · 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

Quick stats

Citations252
Published2007
Admission routes1
Has abstractyes

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