MétaCan
Menu
Back to cohort
Record W4393856313 · doi:10.1016/j.ekir.2024.02.182

WCN24-987 ORGANIZATION AND STRUCTURES FOR DETECTION AND MONITORING OF CHRONIC KIDNEY DISEASE ACROSS WORLD COUNTRIES AND REGIONS: AN OBSERVATIONAL DATA FROM GLOBAL SURVEY

2024· article· en· W4393856313 on OpenAlexaff
Somkanya Tungsanga, Winston Wing‐Shing Fung, Ikechi G. Okpechi, Feng Ye, Philip Kam‐Tao Li, Anukul Ghimire, Jo‐Ann Donner, Aminu K. Bello

Bibliographic record

VenueKidney International Reports · 2024
Typearticle
Languageen
FieldHealth Professions
TopicArtificial Intelligence in Healthcare
Canadian institutionsUniversity of CalgaryUniversity of Alberta
Fundersnot available
KeywordsMedicineObservational studyKidney diseaseIntensive care medicineEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

Proven lifestyle and therapeutic interventions to reduce risk and slow the progression of chronic kidney disease (CKD) are well-established. There is a compelling need for countries and regions to develop organizational structures that allow for the early identification of people at risk or with CKD who will potentially benefit from these proven interventions. We aimed to report the current status of these programs using global survey data conducted by the International Society of Nephrology (ISN).

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)0.004
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.106
Threshold uncertainty score0.212

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.007
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.249
GPT teacher head0.504
Teacher spread0.255 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueKidney International ReportsSame topicArtificial Intelligence in HealthcareFrench-language works237,207