MétaCan
Menu
Back to cohort
Record W4390771318 · doi:10.1158/1055-9965.24967121

Supplementary Table 4 from Male-Pattern Vertex Baldness Trajectories, Chest Hair Patterns, and Odds of Overall and Aggressive Prostate Cancer

2024· supplementary-materials· en· W4390771318 on OpenAlexaboutno aff
Charlotte Salmon, Miceline Mésidor, Marie‐Claude Rousseau, Hugues Richard, Deborah Weiß, Andrea R. Spence, Marie‐Élise Parent

Bibliographic record

Venuenot available
Typesupplementary-materials
Languageen
FieldNursing
TopicNuts composition and effects
Canadian institutionsnot available
Fundersnot available
KeywordsMale-pattern baldnessProstate cancerMedicineOddsOncologyOdds ratioCancerInternal medicineDermatologyScalpLogistic regression

Abstract

fetched live from OpenAlex

Supplementary Table 4. Association between joint exposure of baldness and chest hair amount, and odds of prostate cancer, overall and by cancer aggressiveness, after restricting to participants who had been screened for prostate cancer in the 2 years preceding the index date, Prostate Cancer & Environment Study, Montreal, Canada, 2005–2012

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)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.317
Threshold uncertainty score0.452

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.031
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0030.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.6830.066

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 head0.009
GPT teacher head0.258
Teacher spread0.249 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

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
Published2024
Admission routes1
Has abstractyes

Explore more

Same topicNuts composition and effectsFrench-language works237,207