Making a link between childhood physical abuse and cancer
Bibliographic record
Abstract
BACKGROUND: Abuse in childhood is associated with many negative adult health outcomes. Only 1 study to date has found an association between childhood abuse and cancer. By using a regionally representative community sample, this preliminary study sought to investigate the association between childhood physical abuse and cancer while controlling for 3 clusters of risk factors: childhood stressors, adult health behaviors, and adult socioeconomic status. METHODS: Regional data from the Canadian provinces of Manitoba and Saskatchewan were selected from the 2005 Canadian Community Health Survey. Of the 13,092 respondents, 7.4% (n = 1025) reported that they had been physically abused as a child by someone close to them, and 5.7% (95% confidence interval [CI], 4.9-6.6) reported that they had been diagnosed with cancer by a health professional. The regional level response rate was 84%. RESULTS: Childhood physical abuse was associated with 49% higher odds (95% CI, 1.10-2.01) of cancer when adjusting for age, sex, and race only. The odds ratio decreased only slightly to 47% higher odds (95% CI, 1.05-1.99) when the model was adjusted for all 3 clusters of risk factors. CONCLUSIONS: A significant and highly stable association between childhood physical abuse and cancer was found even when adjusting for 3 clusters of risk factors. Further research focusing on the potential mechanisms linking childhood abuse and cancer is needed.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".