Current Status and Problems of Breast Cancer Treatment with Schizophrenia
Bibliographic record
Abstract
BACKGROUND: Schizophrenia is a devastating mental disease that affects approximately 1% of the world's population. Breast cancer is the second most common type of cancer in the world that causes death in women. It is often unclear whether patients with schizophrenia receive recommended cancer treatment that met the guideline. This study characterized breast cancer treatment disruptions in schizophrenia patients and sought to identify and resolve correctable predictors of those disruptions. MATERIALS AND METHODS: A retrospective cohort study was conducted on 55 primary breast cancer patients diagnosed with schizophrenia and treated for breast cancer. We evaluated the characteristics of the breast cancer patients with schizophrenia compared to those of 610 breast cancer patients without schizophrenia. RESULTS: Compared to the control group, the schizophrenia group had significantly advanced T and N factors and disease stage. Significantly fewer patients in the schizophrenia group than in the control group received chemotherapy (P < .0001) or recommended cancer treatment (P = .0004). Within the schizophrenia group, the patients in need of ADL support were significantly less likely to receive recommended cancer treatment. CONCLUSION: Patients with schizophrenia are often diagnosed with breast cancer in advanced stages. In addition, patients with schizophrenia with reduced ADL are less likely to receive chemotherapy or recommended cancer treatment. It is highly recommended that patients with schizophrenia undergo breast cancer screening so that they can be diagnosed early and treated adequately.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".