Patient Reported Fatigue Among Adolescent and Young Adult Cancer Patients Compared to Non-Cancer Patients: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Adolescent and young adult (AYA) cancer patients and survivors are a growing population due to more frequent diagnoses and improved survival. Fatigue is a common symptom experienced by cancer patients and it is often missed by health care professionals. Patient reported outcome measures (PROMs) can assist in evaluating patient reported fatigue. This systematic review aims to determine if AYA cancer patients report more fatigue than AYAs who have not been diagnosed with cancer. We used a subset of articles from a larger review that searched PubMed, EMBASE, CINAHL, and PsycINFO to determine which PROMs and domains are currently being used to evaluate AYA cancer. This study identified 175 articles related to PROMs in the AYA cancer population. Articles with PROMs reporting on fatigue/vitality were used in this review. From the original 175 articles, we identified 8 fatigue/vitality articles for this review. All eight articles found an increase in fatigue/decrease in vitality in the AYA cancer population compared to healthy controls. A meta-analysis was performed on four articles that used the same PROM tool (EORTC QLQ-C30). This found a statistically significant and clinically meaningful increase in mean fatigue of 12.5 95% confidence interval: 3.3-21.8 points (scale 0-100, higher number indicates more fatigue) in the AYA cancer group compared to healthy noncancer controls. Fatigue in the AYA cancer population is a significant issue, it is often undetected and underreported, and early interventions are needed to prevent the negative subsequent sequelae.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.029 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".