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Record W2908402938 · doi:10.1016/j.kint.2018.08.019

The International Society of Nephrology’s International Consortium of Collaborators on Chronic Kidney Disease of Unknown Etiology: report of the working group on approaches to population-level detection strategies and recommendations for a minimum dataset

2018· editorial· en· W2908402938 on OpenAlexaff
Ben Caplin, Chih‐Wei Yang, Shuchi Anand, Adeera Levin, Magdalena Madero, Rajiv Saran, Saroj Jayasinghe, Marc De Broe, Karen Yeates, Marcello Tonelli, Kristina Jakobsson, Luisa Strani, Agnese Ruggiero, Jason Glaser, Ekti Martin, Neil Pearce, Eranga Wijewickrama

Bibliographic record

VenueKidney International · 2018
Typeeditorial
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Systems and Reforms
Canadian institutionsUniversity of CalgaryQueen's UniversityUniversity of British Columbia
FundersMedical Research Council
KeywordsKidney diseaseMedicinePopulationDiseaseEtiologyEnvironmental healthPathologyInternal 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)0.063
metaresearch head score (Gemma)0.236
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: Editorial · Consensus signal: Editorial
Teacher disagreement score0.063
Threshold uncertainty score0.335

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0630.236
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0080.005
Bibliometrics0.0090.006
Science and technology studies0.0040.004
Scholarly communication0.0110.008
Open science0.0070.004
Research integrity0.0190.033
Insufficient payload (model declined to judge)0.0090.007

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 head0.086
GPT teacher head0.301
Teacher spread0.215 · 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

Citations75
Published2018
Admission routes1
Has abstractno

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