Cancer incidence in parents who lost a child
Bibliographic record
Abstract
BACKGROUND: It has been debated whether psychological stress causes cancer, but the scientific evidence remains contradictory. The objective of this study was to investigate whether the death of a child is related to cancer risk in bereaved parents. METHODS: The authors undertook a follow-up study based on national registers. All 21,062 parents who lost a child from 1980 to 1996 were recruited for the exposed cohort together with 293,745 randomly selected, unexposed parents. Cox proportional hazards regression models were used to evaluate the relative risk of cancer incidence up to 18 years after the bereavement. The main outcomes of interest were all incident cancers, breast carcinoma, smoking-related malignancies (International Classification of Diseases [ICD] 7 codes 140, 141, 143-149, 150, 157, 160-162, 180, and 181), alcohol-related malignancies (ICD7 codes 141, 143-146, 148-150, 155, and 161), virus/immune-related malignancies (ICD7 codes 155, 171, 191, 200-202, and 204), lymphatic/hematopoietic malignancies (ICD7 codes 200-205), and hormone related malignancies (ICD7 codes 170, 172, 175, and 177). RESULTS: The authors observed a slightly increased overall cancer risk in bereaved mothers (relative risk [RR], 1.18; 95% confidence interval [95%CI], 1.01-1.37; P = 0.028) at 7-18 years of follow-up. There was an increased risk for smoking-related malignancies (RR, 1.65; 95%CI, 1.05-2.59; P = 0.010) among bereaved mothers during the 7-18 years of follow-up. The authors observed no significantly increased relative risk of breast carcinoma, alcohol-related malignancies, virus/immune-related malignancies, or hormone-related malignancies. CONCLUSIONS: The current data suggest that the death of a child was associated with a slightly increased overall cancer risk in mothers and that the increase may be related to stress-induced adverse life styles.
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 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".