Bibliographic record
Abstract
PURPOSE OF REVIEW: The cancer mortality rate in persons with schizophrenia is higher than it is in the general population. The purpose of this review is to determine why, and to identify solutions. RECENT FINDINGS: The recent literature points to three groups of reasons why mortality is high: patient reasons such as nonadherence to treatment, provider reasons such as diagnostic overshadowing, and health system reasons such as a relative lack of collaboration between medicine and psychiatry. Strategies for cancer prevention, early detection, and effective treatment are available but difficult to put into practice because of significant barriers to change, namely poverty, cognitive and volitional deficits, heightened stress, stigma, and side effects of antipsychotic medication. The literature makes recommendations about surmounting these barriers and also offers suggestions with respect to support and palliative care in advanced stages of cancer. Importantly, it offers examples of effective collaboration between mental health and cancer care specialists. SUMMARY: The high mortality rate from cancer in the schizophrenia population is a matter of urgent concern. Although reasons are identifiable, solutions remain difficult to implement. As we work toward solutions, quality palliative care at the end of life is required for patients with severe mental illness. VIDEO ABSTRACT.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".