Associations Between Cancer-Related Fatigue and Healthcare Use During Cancer Follow-Up Care: A Survey-Administrative Health Data Linkage Study
Bibliographic record
Abstract
Little is known about the impacts of fatigue after cancer treatment, including whether cancer-related fatigue impacts people’s use of healthcare. This study sought to examine how cancer-related fatigue impacts healthcare use after completing cancer treatment. A population-based survey was administered in Nova Scotia, Canada, to examine survivors’ experiences and needs after completing cancer treatment. Respondents included survivors of breast, melanoma, colorectal, prostate, hematologic, and young adult cancers who were 1–3 years post-treatment. Survey responses were linked to cancer registry, physicians’ claims, hospitalization, and ambulatory care data. Data were analyzed descriptively and using regression models. The final study cohort included 823 respondents. Younger respondents reported higher levels of cancer-related fatigue compared to older respondents. More females than males reported cancer-related fatigue. Upon adjusted analyses, those with cancer-related fatigue had lower odds of being discharged to primary care for their cancer-related follow-up (odds ratio = 0.71, p = 0.029). Moreover, those with cancer-related fatigue had 19% higher primary care use (incidence rate ratio = 1.19, p < 0.0001) and 37% higher oncology use (incidence rate ratio = 1.37, p < 0.016) during the follow-up period compared to those without cancer-related fatigue. Providers (oncology and primary care) may require additional support to identify clinically relevant fatigue and refer patients to appropriate resources and services.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".