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Record W2770146423 · doi:10.1159/000484113

Care of the Acute Kidney Injury Survivor

2017· review· en· W2770146423 on OpenAlexaff
Ron Wald, Abhijat Kitchlu, Ziv Harel, Samuel A. Silver

Bibliographic record

Venue˜The œNephron journals/Nephron journals · 2017
Typereview
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsQueen's UniversityUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsMedicineAcute kidney injuryIntensive care medicineKidney diseaseNephrologyHealth carePopulationInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

While the short-term implications of acute kidney injury (AKI) have been known for many years, far less attention has been paid to the health of AKI survivors. This was perhaps fueled by a prevailing wisdom that if a patient was fortunate enough to survive the primary illness associated with AKI, the prognosis for future health was auspicious. More recently, this dogma was challenged by data suggesting that after an episode of AKI, patients remain at risk of experiencing multiple adverse health outcomes. Greater recognition and appreciation of the risks faced by AKI survivors have led to the development of quality improvement initiatives that are supposed to enrich this vulnerable population.

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)0.003
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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.136
GPT teacher head0.463
Teacher spread0.327 · 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

Citations9
Published2017
Admission routes1
Has abstractyes

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