Temporal order of cancers and mental disorders in an adult population
Bibliographic record
Abstract
BACKGROUND: Population-based examination of comorbidity is an emerging field of study. AIMS: The purpose of the present population level study is to expand our understanding of how cancer and mental illness are temporally associated. METHOD: A sample of 83 648 056 physician billing records for 664 838 (56% female) unique individuals over the age of 18 was stratified on ages 19-49 years and 50+ years, with temporal order of mental disorder and cancer forming the basis of comparison. RESULTS: Mental disorders preceded cancers for both genders within each age strata. The full range of cancers and mental disorders preceding or following each pivot ICD class are described in terms of frequency of diagnosis and duration in days, with specific examples illustrated. CONCLUSIONS: The temporal comorbidity between specific cancers and mental disorders may be useful in screening or clinical planning and may represent indicators of disease mechanism that warrant further screening or investigation. DECLARATION OF INTEREST: None.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".