End-of-life care for cancer patients with pre-existing severe mental disorders—a systematic review
Bibliographic record
Abstract
BACKGROUND: Cancer patients with pre-existing severe mental disorders (SMDs) less frequently receive guideline recommended cancer treatment and have a higher cancer mortality. However, knowledge is needed concerning end-of-life care in this patient group. The aim of this systematic review was to provide an overview of the literature concerning end-of-life care in cancer patients with pre-existing SMD. METHODS: A systematic search was conducted in the following databases: PubMed, Embase and Science Direct and all results were downloaded to Endnote on 1st of September 2023. The review was registered on International Prospective Register of Systematic Reviews (PROSPERO) (ID: CRD42023468571). The quality of the studies was assessed according to the Newcastle-Ottawa Scale. RESULTS: Ten studies fulfilling the inclusion criteria were included. There was a recurring pattern indicating a difference between the end-of-life care received by cancer patients with SMD, compared to those without. Cancer patients with pre-existing SMD received more palliative end-of-life care but less high-intensive-end-of-life (HIEOL) care, e.g., less hospitalisations and chemotherapy at the end of life, and died less frequently at hospital. CONCLUSIONS: The study indicates that patients with pre-existing SMD and cancer more often received palliative end-of-life care and less HIEOL care compared to controls. Further research regarding the difference in end-of-life care is lacking, including the consequences of less intense HIEOL care for this patient group. Thus, further studies are needed to identify reasons for less intense HIEOL among cancer patients with pre-existing SMD.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".