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Record W4380423205 · doi:10.1111/epi.17680

Unjustified allegation on cancer risks in children of mothers with epilepsy taking high‐dose folic acid during pregnancy—No proof of a causal relationship

2023· letter· en· W4380423205 on OpenAlexaff
Randi von Wrede, Juri‐Alexander Witt, Stéphane Auvin, Anita Devlin, Lieven Lagae, Anthony G Marson, Kimford J. Meador, Terence J. O’Brien, Jun Park, Rainer Surges, Eugen Trinka, Samuel Wiebe, Christoph Helmstaedter

Bibliographic record

VenueEpilepsia · 2023
Typeletter
Languageen
FieldMedicine
TopicPharmacological Effects and Toxicity Studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsNewcastle upon tyneMedicineAllegationFamily medicineLibrary sciencePediatricsHistoryPolitical scienceArt historyLawComputer science

Abstract

fetched live from OpenAlex

A recently published paper by Vegrim and colleagues 1 suggests that high-dose folic acid intake during pregnancy in women with epilepsy is associated with an increased risk of cancer in their offspring.The conclusion is drawn from data from an epidemiological study that analyzed large health care databases from Norway, Sweden, and Denmark.The study results have been noted by the public media with spreading concerns of a causal relationship.There are at least 15 million women with epilepsy of childbearing age worldwide for whom folic acid supplementation is recommended by evidence-based guidelines to reduce adverse fetal outcomes.2-5 A reported increase

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)0.004
metaresearch head score (Gemma)0.030
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: Editorial · Consensus signal: none
Teacher disagreement score0.036
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.030
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0030.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0360.020
Insufficient payload (model declined to judge)0.0060.002

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 head0.059
GPT teacher head0.334
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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

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

Citations16
Published2023
Admission routes1
Has abstractyes

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