Symptom severity and trajectories among adolescent and young adult patients with cancer
Bibliographic record
Abstract
BACKGROUND: Patients with cancer experience significant symptom burden. We investigated symptom severity in adolescents and young adults (18- to 39-year-olds) during the year following a cancer diagnosis and made comparisons with older adult (those older than 40 years of age) patients with cancer. METHODS: All Albertan residents diagnosed with a first primary neoplasm at 18 years of age or older between April 1, 2018, and December 31, 2019, and who completed at least 1 electronic patient-reported outcome questionnaire were included. Symptom severity was assessed using the Edmonton Symptom Assessment System-revised. Descriptive statistics, multivariable logistic modeling, and mixed logistic regression modeling were used to describe symptom severity, identify risk factors, and assess symptom trajectories, respectively. RESULTS: In total, 473 and 322 adolescents and young adults completed a patient-reported outcomes questionnaire at diagnosis and 1 year after diagnosis, respectively. Adolescent and young adult patients with cancer reported high levels of tiredness, poor well-being, and anxiety. Important risk factors included metastatic disease, female sex, treatment types received, and age at diagnosis. Symptom severity varied by clinical tumor group, with those diagnosed with sarcoma having the worst scores for all symptoms at diagnosis and patients with intrathoracic or endocrine tumors having the worst scores for all symptoms at 1 year after diagnosis. Statistically significant differences in symptom severity over the 1-year period were observed between adolescents and young adults and older adults-specifically, the odds of having moderate to severe symptoms were statistically significantly greater among adolescents and young adults with respect to pain, tiredness, nausea, depression, anxiety, and poor well-being (all P < .01). CONCLUSIONS: A substantial proportion of adolescents and young adults experience moderate to severe symptoms during the year following diagnosis. Modifying existing supportive services and developing interventions based on the needs of adolescent and young adult patients with cancer could aid symptom control.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".