Patient reported symptoms after cancer diagnosis and the risk of short- and long-term severe mental health events among adolescents and young adults (AYA): A population-based study.
Bibliographic record
Abstract
12019 Background: AYA with cancer are a vulnerable sub-population at risk of adverse mental health outcomes during and after cancer treatment. Tools to identify AYA at highest risk are required to guide screening and interventions. In a population-based cohort of AYA with cancer, we determined whether self-reported symptoms were associated with subsequent short- and long-term severe mental health events (SMHE). Methods: All Ontario, Canada AYA diagnosed with cancer aged 15-29 between 2010-2018 were identified and linked to healthcare databases, including one capturing self-reported Edmonton Symptom Assessment System (ESAS) scores at cancer-related visits. Scores for depression, anxiety, and poor well-being were categorized as not measured, mild (0-3), moderate (4-6), or severe (7-9). SMHE were defined as emergency room visits or hospitalizations for mental health reasons. First, we used Cox proportional hazard models to determine the association of ESAS scores (time-varying variable) with subsequent SMHE. Second, among 5-year survivors, we determined the association of maximum ESAS score within the first year of diagnosis with long-term SMHE (i.e. starting at 5 years from cancer diagnosis). All analyses were adjusted for patient and disease variables, including mental healthcare use prior to cancer diagnosis. Results: 5,435 AYA met inclusion criteria. Median age at cancer diagnosis was 25 years [interquartile range 22-27]. Hematologic cancers were most common (1,748; 32.2%). Symptom severity was associated with subsequent SMHE risk. For example, AYA reporting severe anxiety were at more than three-fold higher risk of SMHE compared to those reporting mild anxiety [adjusted hazard ratio (aHR) 3.6, 95th confidence interval (CI) 1.9-6.7; p < 0.001]. Similar risk was seen among AYA reporting severe vs. mild depression (aHR 3.5, 1.7-7.3; p < 0.001). Among 3,518 (64.7%) 5-year survivors, symptom severity also predicted long-term SMHE. For example, starting at 5 years post cancer diagnosis, the subsequent 3-year cumulative incidence of a SMHE among those who reported severe depression at any time during the first year post cancer diagnosis was 10.5% (95CI 6.9-15.9) compared to 2.4% (95CI 1.7-3.3) among those who only reported mild depression (aHR 3.0, 95CI 1.8-4.9; p < 0.0001). Similar results were seen pertaining to severe anxiety and severe impact on well-being. AYA endorsing severe anxiety represented 13.1% of the cohort but accounted for 25.8% of AYA experiencing SMHEs during the first three years of survivorship. Conclusions: Systematic symptom screening in the first year after cancer diagnosis identifies a proportion of AYA at high risk of both short and long-term SMHE who may benefit from targeted screening and interventions. Future work will determine whether interventions during cancer treatment mitigate this risk.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".