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Risks of Ovarian, Breast, and Corpus Uteri Cancer in Women Treated With Assisted Reproductive Technology in Great Britain, 1991–2010: Data Linkage Study Including 2.2 Million Person Years of Observation

2019· article· en· W2914014681 on OpenAlexaff
Carrie Williams, Michael E. Jones, Anthony J. Swerdlow, Beverley Botting, Melanie Davies, Ian Jacobs, Kathryn Bunch, Michael Murphy, Alastair Sutcliffe

Bibliographic record

VenueObstetrical & Gynecological Survey · 2019
Typearticle
Languageen
FieldMedicine
TopicCancer Risks and Factors
Canadian institutionsInstitute of Cancer Research
Fundersnot available
KeywordsMedicineGynecologyOvarian cancerObstetricsCorpus UteriBreast cancerCancerOncologyInternal medicine

Abstract

fetched live from OpenAlex

(Abstracted from BMJ 2018;362:k2644) Assisted reproductive therapy such as exposure of women to supraphysiological levels of estradiol, exogenous gonadotropins, and multiple ovarian punctures are suspected to incur an increased risk of cancer. Studies investigating the carcinogenic effects of these exposures are largely inconsistent.

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)0.002
metaresearch head score (Gemma)0.006
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.169
Threshold uncertainty score0.336

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.008
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.170
GPT teacher head0.355
Teacher spread0.185 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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