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Record W2778215784 · doi:10.1017/s104795111700213x

Landmark lecture on cardiac intensive care and anaesthesia: continuum and conundrums

2017· article· en· W2778215784 on OpenAlexaff
Peter C. Laussen

Bibliographic record

VenueCardiology in the Young · 2017
Typearticle
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMedicineLandmarkIntensive care medicineAnesthesiaArtificial intelligence

Abstract

fetched live from OpenAlex

Cardiac anesthesia and critical care provide an important continuum of care for patients with congenital heart disease. Clinicians in both areas work in complex environments in which the interactions between humans and technology is critical. Understanding our contributions to outcomes (modifiable risk) and our ability to perceive and predict an evolving clinical state (low failure-to-predict rate) are important performance metrics. Improved methods for capturing continuous physiologic signals will allow for new and interactive approaches to data visualization, and for sophisticated and iterative data modeling that will help define a patient's phenotype and response to treatment (precision physiology).

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)0.004
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.003
Scholarly communication0.0030.004
Open science0.0020.003
Research integrity0.0060.016
Insufficient payload (model declined to judge)0.0190.009

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 head0.013
GPT teacher head0.274
Teacher spread0.261 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2017
Admission routes1
Has abstractyes

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