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Record W1974610305 · doi:10.1155/2014/725748

Early Critical Care Course in Children after Liver Transplant

2014· article· en· W1974610305 on OpenAlexaff
Vinay Kukreti, Hani Daoud, Sundeep S. Bola, Ram N. Singh, Paul Atkison, Alik Kornecki

Bibliographic record

VenueCritical Care Research and Practice · 2014
Typearticle
Languageen
FieldMedicine
TopicOrgan Transplantation Techniques and Outcomes
Canadian institutionsChildren’s Health Research InstituteHospital for Sick ChildrenSickKids FoundationUniversity of TorontoLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsMedicineCourse (navigation)Life course approachPediatricsFamily medicineDevelopmental psychologyPsychology

Abstract

fetched live from OpenAlex

Objective. To review the critical care course of children receiving orthotopic liver transplantation (OLT). Methods. A retrospective chart review of patients admitted to the pediatric critical care following OLT performed in our center between 1988 and 2011. Results. A total of 149 transplants in 145 patients with a median age of 2.7 (IQR 0.9-7) years were analyzed. Mortality in the first 28 days was 8%. The median length of stay (LOS) was 7 (4.0-12.0) days. The median length of mechanical ventilation (MV) was 3 (1.0-6.2) days. Open abdomen, age, and oxygenation index on the 2nd day predicted LOS. Open abdomen, age, amount of blood transfused during surgery, and PRISM III predicted length of MV. 28% of patients had infection and 24% developed acute rejection. In recent group (2000-2011) OLT was performed in younger patients; the risk of infection and acute rejection was reduced and patients required longer LOS and MV compared with old group (1988-1999). Conclusion. The postoperative course of children after OLT is associated with multiple complications. In recent years OLT was performed in younger children; living donors were more common; the rate of postoperative infection and suspected rejection was reduced significantly; however patients required longer MV and LOS in the PCCU.

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 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)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.083
Threshold uncertainty score0.474

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.047
GPT teacher head0.444
Teacher spread0.396 · 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 teacher head, 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

Citations16
Published2014
Admission routes1
Has abstractyes

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