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Record W3215870662

Conceptual Framework for A Perinatal Decision Support System using a Knowledge-Based Approach

2012· article· en· W3215870662 on OpenAlex
Marry Gunaratnam, Monique Frize, Erika Bariciak

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

VenueCMBES Proceedings · 2012
Typearticle
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsUniversity of OttawaCarleton University
Fundersnot available
KeywordsClinical decision support systemWorkflowKnowledge baseComputer scienceDecision support systemKnowledge-based systemsKnowledge managementAccreditationKnowledge extractionProcess managementData miningDatabaseWorld Wide WebMedicineEngineering
DOInot available

Abstract

fetched live from OpenAlex

This paper discusses the development of a knowledge based perinatal clinical decision support system (CDSS) to predict preterm labour. It consists of a knowledge-base, a workflow engine, and a mechanism to communicate results. The knowledge base contains rules or associations related to the desired predictions; the workflow engine combines the rules in the knowledge base with the patient data; and the communication mechanism allows entry of the patient data into the system, and output of results in the form of notifications, alerts or emails. This system will help physicians to inform families and to initiate preventative care, monitoring, and treatment. The final form of the CDSS is to be integrated to an electronic medical record (EMR) and thus allow for auto-population of the patient data into appropriate fields. A web-based collaborative platform that meets the legal and regulatory accreditation standards will be used to deliver information relevant to clinical users.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.767
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.124
GPT teacher head0.450
Teacher spread0.326 · 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