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Record W2411045732

Extended donor ischemic times and recipient outcome after orthotopic cardiac transplantation.

2001· article· en· W2411045732 on OpenAlexaffabout
Mullen Jc, Bentley Mj, Modry Dl, Arvind Koshal

Bibliographic record

VenuePubMed · 2001
Typearticle
Languageen
FieldMedicine
TopicTransplantation: Methods and Outcomes
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineHeart transplantationCardiopulmonary bypassTransplantationSurgeryRetrospective cohort studyIschemiaCardiologyInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: The effect of extended donor ischemic times on mortality following heart transplantation is a matter of considerable debate. PATIENTS AND METHODS: A retrospective study of the 261 consecutive heart transplantations performed at the centre (University of Alberta, Edmonton, Alberta) between July 1985 and June 1999 was conducted. Patients were divided into the following two groups based on donor ischemic time: 4 h or less and longer than 4 h. Donor and recipient factors were analyzed for their effects on 30-day and 90-day survival. RESULTS: Thirty-day mortality was not significantly greater with prolonged donor ischemic times (13%) than with shorter ischemic times (7%, P=0.14). There was also no significant increase in 90-day mortality with longer ischemic times (16%) than with shorter ischemic times (10%, P=0.27). Actuarial survival (10 years) was similar between the groups (P=0.33). Predictors of 30-day and 90-day mortality were cardiopulmonary bypass time (P<0.001 and P<0.001, respectively) and lower donor weight (P=0.008 and P=0.02, respectively). CONCLUSIONS: Longer donor ischemic times were not significantly related to decreased 30-day, 90-day or 10-year actuarial survival.

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)0.002
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
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)0.0020.000

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 head0.025
GPT teacher head0.278
Teacher spread0.253 · 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

Citations14
Published2001
Admission routes2
Has abstractyes

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