Factors Associated with Sleep Disruption and Fatigue in Thyroid Cancer Survivors
Bibliographic record
Abstract
Thyroid cancer survivors often experience worse quality of life than other cancer survivors, with fatigue and sleep disturbance being common contributors. In this prospective cohort from the ICaRe2 cancer registry, survivors completed the Brief Fatigue Inventory (BFI) and Pittsburgh Sleep Quality Index (PSQI) at enrollment and follow-up, with univariate and multivariable analyses identifying factors associated with fatigue and sleep quality. Among 249 survivors (83% female, median age 42), 205 completed the BFI and 224 the PSQI. Most were low (57%) or intermediate (34%) risk or recurrence at diagnosis, and 74% had no structural recurrence. Poor sleep and greater fatigue were significantly associated with female sex (p = 0.0003 and 0.001), younger age at diagnosis (p = 0.02 and 0.0006), and vocal cord paralysis (p = 0.01 and 0.046). Fatigue was also higher in those with hypoparathyroidism (p = 0.04). No associations were found with recurrence risk, therapy response, thyroid hormone type, or TSH levels. Younger female survivors, particularly those with vocal cord paralysis or hypoparathyroidism, are more prone to fatigue and poor sleep, highlighting potential targets for interventions to improve quality of life.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".