Underlying sleep pathology may cause chronic high fatigue in shift‐workers
Bibliographic record
Abstract
About 20-25% of the population in primary healthcare settings complains of chronic fatigue but this symptom has been under-emphasized compared with sleepiness in clinical practice. Shift-workers are particularly vulnerable because of various fatigue-related personal and public morbidity and mortality. The goal of this cross-sectional study was to explore if fatigue severity could be used as an independent predictive tool to identify underlying sleep pathology. The 21 most-fatigued (study group) and 23 least-fatigued (control) miners were selected on the basis of the Fatigue Severity Scale (FSS), which was administered to 195 subjects in an underground mine in Timmins, a town in northern Ontario. The two groups were matched for age, gender, and body mass index (BMI). Mean FSS score for the most-fatigued subjects was 4.9 +/- 0.5 and the least-fatigued was 2.2 +/- 0.5 (P < 0.0001). The subjects from each group were studied polysomnographically to identify sleep disorders. The polysomnographic data in 15 of 21 (71.4%) of the most-fatigued subjects displayed significant sleep pathology compared with only three of 23 (13.0%) in the least-fatigued subjects. Based on Fisher's exact test, the difference between the two groups was highly significant (P < 0.0001). Also, in the total subject pool (n = 195), the correlation between subjective fatigue and sleepiness was not very strong (Pearson's r = 0.45), suggesting that these two symptoms can be independent phenomena. It is concluded that chronic high fatigue can be an independent manifestation of underlying sleep pathology, which warrants independent subjective and objective assessment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".