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Relation of Physical Activity to Risk of Testicular Cancer

2000· article· en· W2134989178 on OpenAlexafffundabout
Anil Srivastava, Nancy Kreiger

Bibliographic record

VenueAmerican Journal of Epidemiology · 2000
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Wellbeing Research
Canadian institutionsCancer Care OntarioToronto Public HealthPublic Health OntarioUniversity of Toronto
FundersHealth Canada
KeywordsTesticular cancerMedicineRelation (database)CancerOncologyPhysiologyInternal medicineComputer science

Abstract

fetched live from OpenAlex

In North America and most Western European countries, testicular cancer is often cited as the most common cancer among young and middle-aged men, and yet few studies have examined the relation between modifiable factors and testicular cancer risk. Data collected between 1995 and 1996 in Ontario, Canada, as part of the Enhanced Cancer Surveillance Study were used to examine the relation between the frequency of recreational, and intensity of occupational, physical activity at various life periods, including cumulative and averaged lifetime activity and risk of testicular cancer. Analysis of 212 cases and 251 controls revealed that relatively high frequency of participation in moderate and strenuous recreational activity in the midteens may have an adverse effect on risk of testicular cancer (odds ratio = 2.36, 95% confidence interval: 1.20, 4.64 for moderate activity of greater than five times a week compared with three times or less a month and odds ratio = 2.58, 95% confidence interval: 1.14, 5.85 for strenuous activity of greater than five times a week compared with less than once a month). Moderate or strenuous occupational demands in one's 20s also increased risk of disease.

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)0.000
metaresearch head score (Gemma)0.004
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 score0.096
Threshold uncertainty score0.191

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.088
GPT teacher head0.531
Teacher spread0.443 · 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

Citations51
Published2000
Admission routes3
Has abstractyes

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