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Record W2123920661 · doi:10.1177/1089253213519291

Efficacy of Evolving Early-Extubation Strategy on Early Postoperative Functional Recovery in Pediatric Open-Heart Surgery

2014· article· en· W2123920661 on OpenAlexaff
Barbara C.S. Hamilton, Osami Honjo, Abdullah A. Alghamdi, Christopher A. Caldarone, Steven M. Schwartz, Glen S. Van Arsdell, Helen Holtby

Bibliographic record

VenueSeminars in Cardiothoracic and Vascular Anesthesia · 2014
Typearticle
Languageen
FieldMedicine
TopicCongenital Heart Disease Studies
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsMedicineInotropeAnesthesiaCardiac surgeryHemodynamicsRetrospective cohort studyHeart rateHeart failureSurgeryInternal medicineBlood pressure

Abstract

fetched live from OpenAlex

There has been a paradigm shift toward "fast-track" management with early extubation (EE) in cardiac surgery. Our retrospective, matched case-control study wishes to define the benefits of EE in pediatric congenital heart surgery. We examined 50 consecutive pediatric cardiac surgery patients extubated in the operating room (February 2009 to July 2009) against a control group of delayed-extubation patients. No significant differences were found in preoperative variables except heart failure medication. Significant intraoperative variables included the following: blood products (363 vs 487 mL, P = .023), morphine (62% vs 6%, P < .0001), and inotropes (16% vs 60%, P < .0001) given. Postoperatively significant differences included hospital stay and lower inotrope scores in the early-extubation group (14.89 vs 31.68, P < .0001). The reintubation rate was not significant. EE patients have equivalent hemodynamic profiles shown by a decreased necessity for inotropic support. We conclude that EE is feasible in low-/medium-risk pediatric congenital heart surgery patients.

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)0.001
metaresearch head score (Gemma)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.020
GPT teacher head0.281
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 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

Citations35
Published2014
Admission routes1
Has abstractyes

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