Mental Health Resource Use Among Patients Undergoing Curative Intent Treatment for Bladder Cancer
Bibliographic record
Abstract
BACKGROUND: Patients with bladder cancer may experience mental health distress. Mental health-care service (MHS) use can quantify the magnitude of the problem. METHODS: The Ontario Cancer Registry was used to identify all patients with bladder cancer treated with curative-intent cystectomy or radiotherapy in Ontario, Canada (2004-2013). Population-level databases were used to identify MHS use (visits to general practitioner, psychiatrist, emergency department, or hospitalization). Generalized estimating equations were used to compare rates of MHS use. Baseline, peritreatment, and posttreatment MHS use were defined as visits from 2 years to 3 months before, 3 months before to 3 months after, and from 3 months after to 2 years after start of treatment, respectively. RESULTS: From 2004 to 2013, 4296 patients underwent cystectomy (n = 3332) or curative-intent radiotherapy (n = 964). Compared with baseline, the rate of MHS use was higher in the peritreatment (adjusted rate ratio [aRR] = 1.64, 95% confidence interval [CI] = 1.48 to 1.82) and posttreatment periods (aRR = 1.45, 95% CI =1.30 to 1.63). By 2 years posttreatment, 24.6% (95% CI = 23.4% to 25.9%) of all patients had MHS use. Patients with baseline MHS use had substantially higher MHS use in the peritreatment (aRR = 5.77, 95% CI = 4.86 to 6.86) and posttreatment periods (aRR = 4.58, 95% CI = 3.78 to 5.55). Female patients had higher use MHS use overall, but males had a higher incremental increase in the posttreatment period compared with baseline (2-sided Pinteraction = .02). Male patients had a statistically significant increase in MHS use following surgery or radiotherapy, whereas female patients only had an increase following surgery. CONCLUSIONS: MHS use is common among patients undergoing treatment for bladder cancer, particularly in the peritreatment period. Screening for mental health concerns in this population is warranted.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".