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Record W3092031097 · doi:10.1097/mcc.0000000000000778

New drugs for acute kidney injury

2020· review· en· W3092031097 on OpenAlexaff
Jean Côté, Patrick Murray, Mitchell H. Rosner

Bibliographic record

VenueCurrent Opinion in Critical Care · 2020
Typereview
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsAcute kidney injuryMedicineIntensive care medicineClinical trialBioinformaticsPharmacologyInternal medicineBiology

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: To describe recent advances in the development of therapeutic agents for acute kidney injury (AKI). RECENT FINDINGS: Traditional care for AKI is mostly supportive. At present, no specific therapy has been developed to prevent or treat AKI. However, based on a better understanding of the pathophysiology of AKI, various potential compounds have been recently identified and tested. A variety of pathways has been targeted, including oxidative and mitochondrial stress, cellular metabolism and repair, inflammation, apoptosis and hemodynamics. Many of these potential agents are currently ongoing early-phase clinical trials, and the purpose of this review is to provide a summary of those with the most potential. SUMMARY: Despite the lack of therapies specifically approved for AKI, many interesting potential agents are entering clinical trials, with the potential to transform the care of patients with AKI.

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)0.000
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.553
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.266
GPT teacher head0.572
Teacher spread0.307 · 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 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

Citations12
Published2020
Admission routes1
Has abstractyes

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