Impact of pre‐existing mental health disorders on the receipt of guideline recommended cancer treatments: A systematic review
Bibliographic record
Abstract
OBJECTIVE: Disparities in cancer outcomes for individuals with pre-existing mental health disorders have already been identified, particularly for cancer screening and mortality. We aimed to systematically review the influence on the time from cancer diagnosis to cancer treatment, treatment adherence, and differences in receipt of guideline recommended cancer treatment. METHODS: We included international studies published in English from 1 January 1995 to 23 May 2022 by searching MEDLINE, Embase, and APA PsycInfo. RESULTS: This review identified 29 studies with 27 being published in the past decade. Most studies focused on breast, non-small cell lung and colorectal cancer and were of high or medium quality as assessed by the Newcastle Ottawa Scale. All studies were from high-income countries, and mostly included patients enrolled in national health insurance systems. Five assessed the impact on treatment delay or adherence, and 25 focused on the receipt of guideline recommended treatment. 20/25 studies demonstrated evidence that patients with pre-existing mental health disorders were less likely to receive guideline recommended therapies such as surgery or radiotherapy. In addition, there was a greater likelihood of receiving less intensive or modified treatment including systemic therapy. CONCLUSIONS: Across different cancer types and treatment modalities there is evidence of a clear disparity in the receipt of guideline recommended cancer treatment for patients with pre-existing mental health disorders. The effect of pre-existing mental health disorders on treatment delay or adherence is under-researched. Future research needs to include low- and middle-income countries as well as qualitative investigations to understand the reasons for disparities in cancer treatment.
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 | 0.008 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".