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Record W2511782953 · doi:10.1055/s-0035-1568147

Impact of Electronic Data on the Development of Care in Critically Ill Children

2016· editorial· en· W2511782953 on OpenAlex
Michaël Sauthier, Philippe Jouvet

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

VenueJournal of Pediatric Intensive Care · 2016
Typeeditorial
Languageen
FieldMedicine
TopicHealthcare Technology and Patient Monitoring
Canadian institutionsCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsMedicineIntensive careCritically illIntensive care medicineElectronic medical recordMedical emergencyIntervention (counseling)Electronic dataNursing

Abstract

fetched live from OpenAlex

Major changes are occurring in pediatric intensive care due to the transition from paper to electronic clinical data collection. In this supplement of the Journal of Pediatric Intensive Care, a panel of experts review the literature and report their experience on the progress of this revolution. The electronic platform allows for data to be collected in a systematic way. Matton et al[1] report the customized implementation of a paperless pediatric intensive care electronic medical record (EMR). They used a 20-month preparation period and a living laboratory approach after the "go-live" (continuous monitoring of issues and problems and rapid intervention to correct them). Their report focuses on safety issues and staff satisfaction, both of which are major challenges during such transitions.

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)0.025
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.520
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.003
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 head0.023
GPT teacher head0.364
Teacher spread0.342 · 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