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Record W2042911904 · doi:10.1109/bhi.2012.6211676

Trends and opportunities for integrated real time neonatal clinical decision support

2012· article· en· W2042911904 on OpenAlexaff
Nadja Bressan, Andrew James, Carolyn McGregor

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicHealthcare Technology and Patient Monitoring
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of TorontoCommunications and Information Technology OntarioOntario Tech University
Fundersnot available
KeywordsNeonatal intensive care unitDecision support systemLife supportClinical decision support systemClinical PracticeIntensive care medicinePerspective (graphical)Intensive careComputer scienceIntensive care unitMedicinePediatricsNursingArtificial intelligence

Abstract

fetched live from OpenAlex

Neonatal Intensive Care Unit maintain and support life during the critical period of premature development. This research presents the challenges, trends and opportunities for integrated real time neonatal clinical decision support. We demonstrated this potential using environment known as Artemis, a clinical decision support system. A review of the current devices in the intensive care unit and neonatal practice shows the current environment and our perspective for the future of the neonatal clinical decision support. The study demonstrates that Artemis will be able to incorporate new data streams from infusion pumps, EEG monitors and cerebral oxygenation monitors innovating the practice and improving the clinical support.

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)0.011
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0050.007
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.169
GPT teacher head0.426
Teacher spread0.257 · 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 designTheoretical or conceptual
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

Citations11
Published2012
Admission routes1
Has abstractyes

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