MétaCan
Menu
Back to cohort
Record W2488999448 · doi:10.1016/j.ekir.2016.07.002

Pediatric Nephrology Training Worldwide 2016: Quantum Educatus?

2016· article· en· W2488999448 on OpenAlexaboutno aff
William A. Primack, Dorey A. Glenn, Kevin Meyers

Bibliographic record

VenueKidney International Reports · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsnot available
FundersAmerican Society of Nephrology
KeywordsMedicineNephrologyInternal medicineTraining (meteorology)Intensive care medicine

Abstract

fetched live from OpenAlex

Pediatric nephrology training is, by necessity, extensive. Pediatric nephrologists typically care for highly complex patients in tertiary academic medical centers, where they provide consultation and direct care to inpatients and outpatients and provide vital services to critically ill children. In most settings, practicing pediatric nephrologists also teach medical students, pediatric trainees, and nurses. Preparing neophyte pediatric nephrologists requires extensive education and experience in both inpatient and outpatient settings, as well as exposure to research, research methods, and faculty development.

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)0.005
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.995
Threshold uncertainty score0.184

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0020.001
Scholarly communication0.0060.008
Open science0.0010.009
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0550.010

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 head0.027
GPT teacher head0.303
Teacher spread0.275 · 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 designObservational
DomainIncentives
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

Citations2
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueKidney International ReportsSame topicDiversity and Career in MedicineFrench-language works237,207