MétaCan
Menu
Back to cohort
Record W4250995090 · doi:10.1787/8311090f-en

OECD Health Division Survey on Health Care Provider Payment for Nuclear Medicine Diagnostic Services

2019· book-chapter· en· W4250995090 on OpenAlexaboutno aff

Bibliographic record

VenueOECD eBooks · 2019
Typebook-chapter
Languageen
FieldMedicine
TopicRadiation Dose and Imaging
Canadian institutionsnot available
Fundersnot available
KeywordsScope (computer science)NOMINATEPaymentHealth careMedicineFamily medicineBusinessGeographyEconomic growthFinance

Abstract

fetched live from OpenAlex

The initial geographic scope of this study was defined as the 23 countries that are members of the European Union and the OECD as well as Australia, Canada, Japan and the United States. An invitation to nominate respondents to the OECD Health Division Survey on Health Care Provider Payment for Nuclear Medicine Diagnostic Services was sent in January 2018 all country delegates in the OECD Health Committee. Respondents were nominated in 26 countries, including 22 countries in the initial scope and Iceland, Israel, Norway and Switzerland. All nominated respondents were contacted between April and June 2018. By September 2018, responses were submitted by respondents from 16 countries, including 15 countries that were in the initial geographic scope of the study and Switzerland. Details are presented in the table below.

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)0.001
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.064
Threshold uncertainty score0.127

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.008
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0230.012

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 head0.030
GPT teacher head0.313
Teacher spread0.284 · 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
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueOECD eBooksSame topicRadiation Dose and ImagingFrench-language works237,207