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Record W2919278110 · doi:10.1016/j.ekir.2019.02.015

Predicting Outcomes in Acute Kidney Injury Survivors: Searching for the Crystal Ball

2019· editorial· en· W2919278110 on OpenAlexaff
William Beaubien‐Souligny, Ron Wald

Bibliographic record

VenueKidney International Reports · 2019
Typeeditorial
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsSt. Michael's HospitalCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsMedicineCrystal BallAcute kidney injuryBall (mathematics)Intensive care medicineMedical emergencyInternal medicine

Abstract

fetched live from OpenAlex

For patients initiating dialysis in the setting of acute kidney injury (AKI-D) and their families, the possibility of permanent dialysis dependence is a source of understandable anxiety. The ability to anticipate nonrecovery of kidney function would enable clinicians to adapt care plans to incorporate the components of end-stage kidney disease care (e.g., access optimization, modality choice). The prospect of long-term dialysis dependence may impel some to adopt a more conservative approach and opt to defer dialysis initiation.

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)0.066
metaresearch head score (Gemma)0.301
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: Editorial · Consensus signal: none
Teacher disagreement score0.066
Threshold uncertainty score0.349

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0660.301
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0070.011
Science and technology studies0.0030.003
Scholarly communication0.0090.009
Open science0.0070.007
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0050.001

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 head0.016
GPT teacher head0.359
Teacher spread0.343 · 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
GenreEditorial

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
Published2019
Admission routes1
Has abstractyes

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