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Record W4361880185 · doi:10.1158/1055-9965.22438735

Supplementary Figure S1 from Changing Inequalities in Cervical Cancer: Modeling the Impact of Vaccine Uptake, Vaccine Herd Effects, and Cervical Cancer Screening in the Post-Vaccination Era

2023· preprint· en· W4361880185 on OpenAlexaboutno aff
Talía Malagón, Mélanie Drolet, Marie‐Claude Boily, Jean‐François Laprise, Marc Brisson

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicCervical Cancer and HPV Research
Canadian institutionsnot available
Fundersnot available
KeywordsCervical cancerMedicineCancerVaccinationDemographyHerdCervical cancer screeningInternational agencyHerd immunityPap testBasal cellInternal medicineOncologyVirologyVeterinary medicineSociology

Abstract

fetched live from OpenAlex

Supplementary Figure S1. Screening history of SCC cases by time since last Pap test by age group. Model predictions are the average of 10 posterior parameter sets run 50 times. Observations are the empirical screening history of SCC cases in British Columbia, Canada. SCC=squamous cell carcinoma. Data from: BC Cancer Agency. Cervical Cancer Screening Program 2011 Annual Report. Vancouver, BC2011.

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)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.680
Threshold uncertainty score0.457

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.6800.102

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 head0.065
GPT teacher head0.385
Teacher spread0.320 · 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 designNot applicable
Domainnot available
GenreDataset

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
Published2023
Admission routes1
Has abstractyes

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