Risk Factors for Decreased Quality of Life in Thyroid Cancer Survivors: Initial Findings from the North American Thyroid Cancer Survivorship Study
Bibliographic record
Abstract
BACKGROUND: The prevalence of thyroid cancer survivors is rising rapidly due to the combination of an increasing incidence, high survival rates, and a young age at diagnosis. The physical and psychosocial morbidity of thyroid cancer has not been adequately described, and this study therefore sought to improve the understanding of the impact of thyroid cancer on quality of life (QoL) by conducting a large-scale survivorship study. METHODS: Thyroid cancer survivors were recruited from a multicenter collaborative network of clinics, national survivorship groups, and social media. Study participants completed a validated QoL assessment tool that measures four morbidity domains: physical, psychological, social, and spiritual effects. Data were also collected on participant demographics, medical comorbidities, tumor characteristics, and treatment modalities. RESULTS: A total of 1174 participants with thyroid cancer were recruited. Of these, 89.9% were female, with an average age of 48 years, and a mean time from diagnosis of five years. The mean overall QoL was 5.56/10, with 0 being the worst. Scores for each of the sub-domains were 5.83 for physical, 5.03 for psychological, 6.48 for social, and 5.16 for spiritual well-being. QoL scores begin to improve five years after diagnosis. Female sex, young age at diagnosis, and lower educational attainment were highly predictive of decreased QoL. CONCLUSION: Thyroid cancer diagnosis and treatment can result in a decreased QoL. The present findings indicate that better tools to measure and improve thyroid cancer survivor QoL are needed. The authors plan to follow-up on these findings in the near future, as enrollment and data collection are ongoing.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".