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Record W2072313110 · doi:10.5867/medwave.2015.03.6122

¿Debe indicarse acetilcisteína para prevenir la nefropatía por contraste?

2015· review· es· W2072313110 on OpenAlexaff
Ariel Izcovich, Gabriel Rada

Bibliographic record

VenueMedwave · 2015
Typereview
Languagees
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsCochrane
Fundersnot available
KeywordsMedicineHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

Diagnostic and therapeutic procedures that require the infusion of iodine containing contrast solutions are associated with the risk of contrast-induced nephropathy, a condition that can cause significant morbidity. Acetylcysteine has been proposed as a measure to prevent this condition. Searching in Epistemonikos database, which is maintained by screening 30 databases, we identified 20 systematic reviews including 64 randomized trials. We combined the evidence using meta-analysis and generated a summary of findings table following the GRADE approach. We concluded that even though acetylcysteine might not cause important adverse effects, it does not decrease need for dialysis, mortality or other important outcomes.

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)0.003
metaresearch head score (Gemma)0.009
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 score0.006
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.000
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.108
GPT teacher head0.423
Teacher spread0.315 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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