Epidemiology of cancer‐related fatigue in the Swedish twin registry
Bibliographic record
Abstract
BACKGROUND: Estimates of the prevalence of cancer-related fatigue (CRF) are wide, and data suggest that fatigue is more prevalent among cancer patients than among the general population. However, most studies examining the prevalence of CRF have been hospital-based or clinic-based studies, which often are subject to bias. METHODS: Point prevalence and prevalence odds ratios of fatigue were estimated using data from a large, population-based cohort that was screened for fatigue and linked with national registry-based data about cancer. Prevalence odds ratios and 95% confidence intervals were calculated using logistic regression with general estimating equations. RESULTS: Approximately 23% of cancer registrants reported abnormal fatigue in the previous 6 months, 19% reported abnormal fatigue that lasted for at least 1 month, 14% reported abnormal fatigue that lasted at least 6 months, and 11% reported abnormal fatigue that lasted at least 6 months and caused significant functional impairment. Individuals who were listed in the cancer registry within the last 5 years were more likely to report experiencing fatigue than individuals who were not listed. There was an elevated prevalence of fatigue among those who were registered with carcinomas of the lung, uterine cervix, colon-rectum, ovaries, and prostate. Both women and men who were listed recently in the cancer registry were more likely to experience any level of fatigue than the comparison group. However, a greater proportion of women experienced fatigue relative to men. CONCLUSIONS: A greater proportion of individuals who were listed in a national cancer registry reported experiencing fatigue compared with individuals in the general population.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".