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Record W4406844238 · doi:10.1016/j.ekir.2024.11.1219

WCN25-2787 X SPACES : TOPNOTCH PLATFORM FOR VOICE OF NEPHROLOGY COMMUNITY

2025· article· en· W4406844238 on OpenAlexaff
Manjusha Yadla, Augusto da Conceição Soares, Dilushi Wijayratne, Sibel Gockay Bek, Milagros Llanos Flores, Sabine Karam, Suman Behera, Shubharthi Kar

Bibliographic record

VenueKidney International Reports · 2025
Typearticle
Languageen
FieldHealth Professions
TopicArtificial Intelligence in Healthcare
Canadian institutionsToronto General HospitalToronto Western Hospital
Fundersnot available
KeywordsMedicineNephrologyInternal medicine

Abstract

fetched live from OpenAlex

X Spaces, formerly Twitter spaces, are dynamic platforms for nephrology community in connecting, networking, sharing, and exchange cutting edge information. This platform provides ample opportunities for the global community to interact with specific experts in the field. The International society of Nephrology had started this initiative of X Spaces duringWCN 2022. Since then, X Spaces are being organized every month on a relevant topic, with experts joining the session and interacting with listeners.

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)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.430
Threshold uncertainty score0.813

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0060.005
Open science0.0010.008
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.4300.256

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 head0.132
GPT teacher head0.492
Teacher spread0.361 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2025
Admission routes1
Has abstractyes

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