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Record W4281973310 · doi:10.1111/odi.14270

Association between the second‐ and fourth‐digit ratio and oral squamous cell carcinoma

2022· article· en· W4281973310 on OpenAlexafffund
Yue Ying, Sreenath Madathil, Belinda Nicolau

Bibliographic record

VenueOral Diseases · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSexual Differentiation and Disorders
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersCanadian Institutes of Health ResearchMinistère du Développement Économique, de l’Innovation et de l’Exportation
KeywordsOdds ratioMedicineDigit ratioConfidence intervalConfoundingIn uteroPopulationQuartileCase-control studyLogistic regressionCancerInternal medicineOncologyTestosterone (patch)PregnancyFetusBiologyEnvironmental health

Abstract

fetched live from OpenAlex

OBJECTIVES: Using an established proxy measure of intra-utero testosterone and estrogen levels-the ratio of second- and fourth-digit lengths-we estimated its association with the oral cancer risk among a population from Southern India. MATERIAL AND METHODS: In a hospital-based case-control study, incident oral cancer cases (N = 350) and non-cancer controls (N = 371), frequency-matched by age and sex, were recruited from two major referral hospitals in Kerala, India. Structured interviews collected information on several domains of exposure via detailed life course questionnaires. Digit lengths were measured using a ruler in a standardized manner. Unconditional logistic regression was performed to estimate the odds ratios and 95% confidence intervals. RESULTS: Second- and fourth-digit ratio lower than 1, which indicates relatively higher intra-utero level of testosterone and lower intra-utero level of estrogen, was associated with higher oral cancer risk (OR = 1.60, 95% CI: 1.02-2.52), after accounting for several confounders. CONCLUSION: Our findings suggest that intra-utero hormonal levels measured by second- and fourth-digit ratio are associated with oral cancer risk. Further studies in different population should confirm these results.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.020
Threshold uncertainty score0.265

Codex and Gemma teacher scores by category

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

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 head0.011
GPT teacher head0.232
Teacher spread0.222 · 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 teacher head, 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

Citations1
Published2022
Admission routes2
Has abstractyes

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