A Pilot Study Examining Energy Conservation for Cancer Treatment–related Fatigue
Bibliographic record
Abstract
The purpose of this pilot study was to examine the feasibility of conducting an energy conservation and activity management (ECAM) intervention for cancer treatment-related fatigue and describe patterns of cancer treatment-related fatigue for two groups undergoing active treatment, one receiving the ECAM intervention and a nonequivalent control group receiving standard care for cancer treatment-related fatigue. The ECAM group received 3 telephone sessions focusing on the provision of information about fatigue, development of an energy conservation plan, and evaluation of the plan's effectiveness. Data for the ECAM group were collected before treatment, at an expected fatigue high point during treatment, and an expected low point of fatigue after treatment. The nonequivalent control group lacked the pretreatment measure but had equivalent follow-up measurement points. The feasibility of conducting the ECAM intervention was supported by patient adherence in receiving all 3 sessions of the intervention and by their self-reports of its usefulness and plans to continue using ECAM skills. Patterns of fatigue differed for the ECAM study group and the nonequivalent control group, suggesting that the intervention moderates the expected rise in fatigue due to cancer therapy. A full-scale clinical trial is needed to evaluate the efficacy of the ECAM intervention.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".