A Systematic Review of Randomized Controlled Trials for Management of Persistent Post-Treatment Fatigue in Thyroid Cancer Survivors
Bibliographic record
Abstract
BACKGROUND: Fatigue that persists post-treatment is commonly reported by thyroid cancer (TC) survivors. METHODS: A systematic review of published English language randomized controlled trials (RCTs) on interventions for management of persistent post-treatment fatigue in TC was conducted. This review excluded studies on short-term interventions used in preparation for radioactive iodine diagnostic scans or treatment. An electronic search was executed in six databases and supplemented by a hand search. Two reviewers independently reviewed all citations from the electronic search and relevant full-text studies. Two abstractors independently critically appraised included studies and abstracted the data. The data were qualitatively summarized. RESULTS: A total of 1086 unique citations and 25 full-text studies were reviewed. Four studies summarizing the results of three RCTs were included. The interventions included: combination triiodothyronine with levothyroxine (L-T4) therapy compared to L-T4 alone (one RCT), reduction in degree of thyrotropin (TSH) suppression using L-T4 compared to maintenance of TSH suppression (one RCT), and supervised exercise compared to inactivity (two RCTs examining different fatigue outcomes in same population). Trial duration ranged from 10 weeks to six months. All trials had limitations, and the number of TC survivors included in respective RCTs ranged from 15 to 36. Hormonal treatment RCTs had mixed fatigue outcome results within respective trials. However, multiple measures suggesting improvement in fatigue were reported following the exercise intervention. CONCLUSIONS: There is paucity of RCTs to guide evidence-based management of persistent post-treatment fatigue in TC survivors. RCTs of interventions for prevention or treatment of fatigue in TC survivors are needed.
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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 | 0.032 | 0.125 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.013 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".