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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.

2024· article· en· W4399618865 on OpenAlexafffundabout
Sumit Gupta, Qing Li, Rinku Sutradhar, Natalie G. Coburn

Bibliographic record

VenueJournal of Clinical Oncology · 2024
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreInstitute for Clinical Evaluative SciencesHospital for Sick Children
FundersCanadian Institutes of Health ResearchTerry Fox Research Institute
KeywordsMedicineCancerYoung adultMental healthPediatricsPopulationPsychiatryGerontologyEnvironmental healthInternal medicine

Abstract

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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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.250
Threshold uncertainty score0.497

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.044
GPT teacher head0.430
Teacher spread0.386 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes3
Has abstractyes

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