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Record W3193285410 · doi:10.1016/j.kint.2021.07.029

The state of the global nephrology workforce: a joint ASN–ERA-EDTA–ISN investigation

2021· article· en· W3193285410 on OpenAlex
Kurtis Pivert, Fergus Caskey, Adeera Levin

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueKidney International · 2021
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsUniversity of British Columbia
FundersEuropean Renal Association-European Dialysis and Transplant AssociationInternational Society of NephrologyAmerican Society of Nephrology
KeywordsNephrologyWorkforceJoint (building)Internal medicineMedicineState (computer science)Political scienceComputer scienceEngineeringLaw

Abstract

fetched live from OpenAlex

Chronic kidney disease (CKD) is a global health crisis, affecting 11% to 13% of the world’s population.1,2 Although gaps in the workforce and available training pathways have been explored,3,4 it remains unclear if nephrologist availability, measured as ratios of nephrologists to both the general population and to individuals burdened by CKD, is sufficient for the estimated 850 million individuals with CKD. The scope of kidney health services that nephrologists provide, qualified by local health care environments and practice patterns, may also vary geographically.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.280
Threshold uncertainty score0.599

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.049
GPT teacher head0.402
Teacher spread0.353 · 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