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Record W2497734597 · doi:10.1016/j.vaccine.2016.03.047

Congenital anomalies: Case definition and guidelines for data collection, analysis, and presentation of immunization safety data

2016· article· en· W2497734597 on OpenAlexaff
Malini B. DeSilva, Flor M. Muñoz, Mark McMillan, Alison Tse Kawai, Helen Marshall, Kristine Macartney, Jyoti Joshi, Martina Oneko, Annette Elliott Rose, Helen Dolk, Francesco Trotta, Hans Spiegel, Sylvie Tomczyk, Anju Shrestha, Sonali Kochhar, Elyse O. Kharbanda

Bibliographic record

VenueVaccine · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsCancer Care Nova Scotia
Fundersnot available
KeywordsPresentation (obstetrics)ImmunizationData collectionData presentationMedicinePediatricsImmunologySurgerySociology

Abstract

fetched live from OpenAlex

Guidelines Case definitionଝ Disclaimer: The findings, opinions and assertions contained in this consensus document are those of the individual scientific professional members of the working group.They do not necessarily represent the official positions of each participant's organization (e.g., government, university, or corporation).Specifically, the findings and conclusions in this paper are those of the authors and do not necessarily represent the views of their respective institutions.

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)0.049
metaresearch head score (Gemma)0.110
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.049
Threshold uncertainty score0.262

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0490.110
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0210.013
Science and technology studies0.0030.003
Scholarly communication0.0050.006
Open science0.0060.006
Research integrity0.0060.005
Insufficient payload (model declined to judge)0.0160.008

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 head0.126
GPT teacher head0.382
Teacher spread0.256 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations159
Published2016
Admission routes1
Has abstractyes

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