Post-Exertional Malaise in People with Chronic Cancer-Related Fatigue
Bibliographic record
Abstract
Abstract Context Cancer-related fatigue (CRF) is a distressing and persistent sense of tiredness or exhaustion that interferes with usual functioning. Chronic CRF continues for months after curative cancer treatment is complete. Post-exertional malaise (PEM) is a worsening of symptoms after physical or mental activity, with limited investigations in people with chronic CRF. Objectives The purpose of this study was to identify and describe self-reported incidences of PEM in people with chronic CRF. Methods Participants ( n =18) were eligible if they scored ≤34 on the Functional Assessment of Chronic Illness Therapy-Fatigue scale and had a cancer-related onset of fatigue. Participants completed a brief questionnaire to assess PEM over a 6-month time-frame (the DePaul Symptom Questionnaire – Post-Exertional Malaise; DSQ-PEM). In addition, a maximal exercise test was used to investigate self-reported symptom exacerbation (via an open-ended questionnaire) after strenuous physical exertion. Results On the DSQ-PEM, three participants met previously defined scoring criteria, which included experiencing moderate to very severe symptoms at least half of the time, worsening of fatigue after minimal effort, plus a recovery duration of >24 h. Content analysis of responses to open-ended questionnaires identified five people who experienced a delayed recovery and symptoms of PEM after maximal exercise. Conclusion A subset of people with chronic CRF (up to 33% in this sample) may experience PEM. Exercise specialists and health care professionals working with people with chronic CRF must be aware that PEM may be an issue. Symptom exacerbation after exercise should be monitored, and exercise should be tailored and adapted to limit the potential for harm. Key message This study provides preliminary evidence that a subset of people with chronic cancer-related fatigue experience post-exertional malaise.
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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) | 0.002 | 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".