Access to symptom screening and severe symptom risk among cancer patients with major mental illness
Bibliographic record
Abstract
INTRODUCTION: Cancer symptom screening has the potential to improve cancer outcomes, including reducing symptom burden among patients with major mental illness (MMI). We determined rates of symptom screening with the Edmonton Symptom Assessment System (ESAS-r) and risk of severe symptoms in cancer patients with MMI. METHODS: This retrospective cohort study used linked administrative health databases of adults diagnosed with cancer between 2007 and 2020. An MMI was measured in the 5 years prior to cancer diagnosis and categorized as inpatient, outpatient, or no MMI. Outcomes were defined as time to first ESAS-r screening and time to first moderate-to-severe symptom score. Cause-specific and Fine and Gray competing events models were used for both outcomes, controlling for age, sex, rural residence, year of diagnosis and cancer site. RESULTS: Of 389,870 cancer patients, 4049 (1.0%) had an inpatient MMI and 9775 (2.5%) had an outpatient MMI. Individuals with inpatient MMI were least likely to complete an ESAS-r (67.5%) compared to those with outpatient MMI (72.3%) and without MMI (74.8%). Compared to those without MMI, individuals with an inpatient or outpatient MMI had a lower incidence of symptom screening records after accounting for the competing risk of death (subdistribution Hazard Ratio 0.77 (95% CI 0.74-0.80) and 0.88 (95% CI 0.86-0.90) respectively). Individuals with inpatient and outpatient MMI status consistently had a significantly higher risk of reporting high symptom scores across all symptoms. CONCLUSIONS: Understanding the disparity in ESAS-r screening and management for cancer patients with MMI is a vital step toward providing equitable cancer care.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".