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Record W4386818390 · doi:10.15537/1658-3175.8235

Robust analysis challenges theory that depression and anxiety increase cancer risk

2023· article· en· W4386818390 on OpenAlexaboutno aff

Bibliographic record

VenueSaudi Medical Journal · 2023
Typearticle
Languageen
FieldMathematics
TopicStatistical Methods in Clinical Trials
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDepression (economics)AnxietyPsychiatry

Abstract

fetched live from OpenAlex

Robust analysis challenges theory that depression and anxiety increase cancer risk AUGUST 07, 2023 -Depression and anxiety are thought to increase a person's risk of developing cancer, but research results have been inconclusive.In an analysis of multiple studies from the Netherlands, the United Kingdom, Norway, and Canada, investigators found that depression and anxiety are not linked to higher risks for most types of cancer among this population.The analysis is published by Wiley online in CANCER, a peer-reviewed journal of the American Cancer Society.

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)0.186
metaresearch head score (Gemma)0.492
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.814
Threshold uncertainty score0.982

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1860.492
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.006
Bibliometrics0.0040.004
Science and technology studies0.0020.007
Scholarly communication0.0080.005
Open science0.0040.006
Research integrity0.0030.011
Insufficient payload (model declined to judge)0.0060.001

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 head0.440
GPT teacher head0.524
Teacher spread0.083 · 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 designObservational
DomainMethods
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

Citations0
Published2023
Admission routes1
Has abstractyes

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