Mental healthcare use and severe psychiatric diagnoses in adult survivors of childhood cancer: A population-based study using health services data.
Bibliographic record
Abstract
10565 Background: Though physical late effects in childhood cancer survivors are well documented, their risk for adverse mental health outcomes is less clear; existing evidence is contradictory. Health services data offer an objective method for measuring population-based mental health outcomes. Methods: Using a provincial registry with detailed patient, disease, treatment, and outcome data, we assembled a cohort of all five-year survivors of childhood cancer diagnosed before age 18 years and treated in an Ontario pediatric cancer centre between 1987-2008. Patients were linked to population-based healthcare data capturing inpatient, outpatient, and emergency department (ED) visits. The primary outcome was the rate of mental healthcare visits (primary care, psychiatrist, ED or hospital). Secondary outcomes included the time to a severe mental health event (ED visit, hospitalization, or suicide) both overall and by psychiatric diagnostic categories. Outcomes were compared between survivors and matched controls using recurrent event and survival analyses, and predictors of adverse outcomes modeled. Results: When compared to 20,269 controls, 4,117 survivors had a significantly higher rate of mental health visits [47.1 vs. 36.1 visits/100 person years; adjusted relative rate (RR) 1.3, 95% confidence interval (CI) 1.2-1.5]. Higher rates of visits were associated with female gender (RR 1.4, CI 1.1-1.7; p = 0.008) and adolescent age at diagnosis (RR 2.0, CI 1.3-3.0; p = 0.004). Cancer type, treatment intensity or treatments targeting the central nervous system were not significant predictors. The hazard of a severe mental health event did not differ between survivors and controls. Though rare in both groups, survivors were at increased risk of a severe event due to a psychotic disorder (HR 1.8, CI 1.1-2.8; p < 0.05). Conclusions: Childhood cancer survivors experience higher rates of mental health visits than the general population, but are no more likely to experience a severe mental health event. Their risk is not attributable to a specific diagnosis or aspect of treatment. An increased risk of severe psychotic disorders requires confirmation in other cohorts.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".