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Record W2117431822 · doi:10.1038/ki.2015.110

Executive summary of the KDIGO Controversies Conference on Supportive Care in Chronic Kidney Disease: developing a roadmap to improving quality care

2015· article· en· W2117431822 on OpenAlexaff
Sara N. Davison, Adeera Levin, Alvin H. Moss, Vivekanand Jha, Edwina A. Brown, Frank Brennan, Fliss EM Murtagh, Saraladevi Naicker, Michael J. Germain, Dónal O’Donoghue, Rachael L. Morton, Gregorio T. Obrador

Bibliographic record

VenueKidney International · 2015
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsUniversity of British ColumbiaUniversity of Alberta
Fundersnot available
KeywordsPsychosocialMultidisciplinary approachPalliative careMedicineAdvance care planningKidney diseaseIntensive care medicinePopulationExecutive summaryQuality of life (healthcare)NursingPolitical scienceBusinessPsychiatryEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.041
metaresearch head score (Gemma)0.081
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: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.041
Threshold uncertainty score0.216

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0410.081
Meta-epidemiology (narrow)0.0040.001
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0040.006
Science and technology studies0.0030.002
Scholarly communication0.0140.007
Open science0.0090.007
Research integrity0.0300.030
Insufficient payload (model declined to judge)0.0230.014

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 head0.101
GPT teacher head0.413
Teacher spread0.312 · 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

Citations572
Published2015
Admission routes1
Has abstractno

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