Breast cancer treatment disparities in patients with severe mental illness: A systematic review and meta‐analysis
Bibliographic record
Abstract
OBJECTIVE: The incidence and mortality rates of breast cancer in individuals with pre-existing severe mental illness (SMI), such as schizophrenia, bipolar disorder, and major depression, are higher than in the general population. Reduced screening is one factor but there is less information on possible barriers to subsequent treatment following diagnosis. METHODS: We undertook a systematic review and meta-analysis on access to guideline-appropriate care following a diagnosis of breast cancer in people with SMI including the receipt of surgery, endocrine, chemo- or radiotherapy. We searched for full-text articles indexed by PubMed, EMBASE, PsycInfo and CINAHL that compared breast cancer treatment in those with and without pre-existing SMI. Study designs included population-based cohort or case-control studies. RESULTS: There were 13 studies included in the review, of which 4 contributed adjusted outcomes to the meta-analyses. People with SMI had a reduced likelihood of guideline-appropriate care (RR = 0.83, 95% CI = 0.77-0.90). Meta-analyses were not possible for the other outcomes but in adjusted results from a single study, people with SMI had longer wait-times to receiving guideline-appropriate care. The results for specific outcomes such as surgery, hormone, radio- or chemotherapy were mixed, possibly because results were largely unadjusted for age, comorbidities, or cancer stage. CONCLUSIONS: People with SMI receive less and/or delayed guideline-appropriate care for breast cancer than the general population. The reasons for this disparity warrant further investigation, as does the extent to which differences in treatment access or quality contribute to excess breast cancer mortality in people with SMI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".