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Record W2465843687 · doi:10.3233/978-1-58603-979-0-99

Ontario's Province-wide Paediatric Electronic Health Record

2009· article· en· W2465843687 on OpenAlexaffabout
Andrew Szende

Bibliographic record

VenueStudies in health technology and informatics · 2009
Typearticle
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsOntario Stroke Network
Fundersnot available
KeywordsElectronic health recordHealth recordsMedicinePolitical scienceHealth care

Abstract

fetched live from OpenAlex

The electronic Child Health Network (eCHN) is an advanced implementation of an electronic health record (EHR). This is a case study of the building of an integrated and shared EHR from multiple systems at multiple sites for the benefit of patients and clinicians. Optimal patient care requires that information about a person's overall health and interactions with the health care system be available to the appropriate providers when and where it is needed. eCHN is a pioneering response to the need for such shared records. A key to its success is that it is a flexible, scalable, open, multi-vendor solution designed to work with existing hospital systems and other networks. As Canada's largest functioning integrated EHR, eCHN is serving three million people and transforming the quality of health record keeping and clinical decision-making. eCHN enables health care providers across Ontario to share accurate patient data - in real time.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.056
Threshold uncertainty score0.405

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.006
Science and technology studies0.0050.001
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0280.004

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 head0.052
GPT teacher head0.413
Teacher spread0.361 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2009
Admission routes2
Has abstractyes

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