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Is time on cardiopulmonary bypass during cardiac surgery associated with acute kidney injury requiring dialysis?

2011· article· en· W1542822961 on OpenAlexvenueno aff
Fabio Caramelli, Marco Ranucci, Diego Sangiorgi, Maria Letizia Bacchi Reggiani, Guido Frascaroli, A Zucchelli, Antonio Bellasi, Antonio Santoro

Bibliographic record

VenueHemodialysis International · 2011
Typearticle
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCardiopulmonary bypassDialysisAcute kidney injuryOdds ratioExtracorporeal circulationConfidence intervalHemodialysisConfoundingExtracorporealInternal medicineCardiologyUnivariate analysisObservational studyCardiac surgeryMultivariate analysis

Abstract

fetched live from OpenAlex

It is commonly accepted that the longer the time on extracorporeal cardiopulmonary bypass (CPB), the higher is the likelihood of developing acute renal failure requiring dialysis (ARF-D). Nonetheless, previous works elicited conflicting evidence. We investigated the relationship between CPB duration and ARF-D occurrence. Data were extracted from a large observational study. All factors independently associated with ARF-D were detected. Overall, 11,092 case record forms were analyzed. At the univariate analyses, time on CBP was associated with an increase in the ARF-D risk (odds ratio of fifth vs. first quintile of CBP time: 3.84; 95% confidence interval: 2.58-5.7; P < 0.001). However, after adjusting for confounders, the association between time on CBP and ARF-D lost its statistical significance. In this large dataset, CBP time did not predict ARF-D occurrence. These results might suggest that an accurate risk assessment might be more important than time on CPB in determining ARF-D occurrence.

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)0.010
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.032
GPT teacher head0.288
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 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
Published2011
Admission routes1
Has abstractyes

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