MétaCan
Menu
Back to cohort

Early start on continuous hemodialysis therapy improves survival rate in patients with acute renal failure following coronary bypass surgery

2004· article· en· W2148350928 on OpenAlexvenueno aff
Souichi Sugahara, Hiromichi Suzuki

Bibliographic record

VenueHemodialysis International · 2004
Typearticle
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDialysisHemodialysisCreatinineRenal replacement therapySurgeryCardiac surgeryInternal medicineCardiologyUrology

Abstract

fetched live from OpenAlex

Acute renal failure requiring dialysis therapy after cardiac surgery occurs in 1% to 5% of patients; however, the optimal timing for initiation of dialysis therapy still remains undetermined. To assess the validity of early start of dialysis therapy, we studied the comparative survival between 14 patients who started to receive dialysis therapy when urine volume decreased to less than 30 mL/hr and another group of 14 patients who waited to begin dialysis therapy until the level of urine volume was less than 20 mL/hr for 14 days following coronary bypass graft surgery. Twelve of 14 patients who received early intervention survived. In contrast, only 2 of 14 patients in the late-dialysis group survived. There was a significant difference in survival between the two groups (p < 0.01). There were no significant differences between the two groups with respect to age, sex ratio, the APACHE (Acute Physiologic and Chronic Health Evaluation) II score, and the levels of serum creatinine at the start of dialysis therapy (2.9 +/- 0.2 mg/dL vs. 3.1 +/- 0.2 mg/dL), as well as the levels of serum creatinine at admission. We propose that the timing of the start for treatment of acute renal failure following cardiac surgery should be determined by the decrease of urine volume and not the levels of serum creatinine. Early start of dialysis therapy may help improve the survival of patients with acute renal failure following cardiac surgery.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.015
GPT teacher head0.271
Teacher spread0.256 · 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.

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

Citations138
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueHemodialysis InternationalSame topicAcute Kidney Injury ResearchFrench-language works237,207