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Record W1532088614 · doi:10.1159/000329410

Controversies in Acute Kidney Injury: Effects of Fluid Overload on Outcome

2011· review· en· W1532088614 on OpenAlexaff
Ravindra L. Mehta, Josée Bouchard

Bibliographic record

VenueContributions to nephrology · 2011
Typereview
Languageen
FieldMedicine
TopicTrauma, Hemostasis, Coagulopathy, Resuscitation
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMedicineAcute kidney injuryResuscitationIntensive care medicineCritically illInternal medicineAnesthesia

Abstract

fetched live from OpenAlex

Fluid management in critically ill patients is a complex process as aggressive fluid resuscitation is commonly utilized for initial hemodynamic support and fluid administration often contributes to fluid retention, particularly when there is impaired kidney function. Recent evidence suggests that fluid accumulation is associated with adverse outcomes. It is unclear whether fluid retention is simply a marker of the severity of organ failure or a mediator of events. In this article we review the evidence and provide a framework for future studies to refine these concepts further.

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)0.005
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0040.005
Science and technology studies0.0010.002
Scholarly communication0.0020.003
Open science0.0020.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.028
GPT teacher head0.372
Teacher spread0.345 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations42
Published2011
Admission routes1
Has abstractyes

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