Bibliographic record
Abstract
OBJECTIVE: To record risk factors for breast cancer in women with schizophrenia and recommend preventive actions. METHOD: A PubMed literature search (from 2005 to 2010) was conducted, using the search terms 'schizophrenia', 'antipsychotics', 'breast cancer' and 'risk factors'. RESULTS: Several risk factors of relevance to schizophrenia were identified: obesity, elevated prolactin levels, low participation in mammography screening, high prevalence of diabetes, comparatively low parity, low incidence of breastfeeding, social disadvantage, high levels of smoking and alcohol consumption, low activity levels. CONCLUSION: Awareness of breast cancer risk should lead to more accurate risk ascertainment, stronger linkage with primary care, regular monitoring and screening, judicious choice and low dose of antipsychotic treatment, concomitant use of adjunctive cognitive and psychosocial therapies, referral to diet and exercise programmes as well as smoking and drinking cessation programmes, avoidance of hormonal treatment and discussion with patient and family about the pros and cons of preventive measures in high-risk women. Psychiatrists are in a position to reverse many of the identified risk factors.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".