Optimal detection of hypothyroidism in early stage laryngeal cancer treated with radiotherapy
Bibliographic record
Abstract
BACKGROUND: Hypothyroidism following radiation therapy (RT) for treatment of Head and Neck Cancer (HNC) is a common occurrence. Rates of hypothyroidism following RT for Early Stage Laryngeal Squamous Cell Carcinoma (ES-LSCC) are among the highest. Although routine screening for hypothyroidism is recommended; its optimal schedule has not yet been established. We aim to determine the prevalence and optimal timing of testing for hypothyroidism in ES-LSCC treated with RT. METHOD: We conducted a population-based cohort study. Data was extracted from a prospective provincial head and neck cancer database. Demographic, survival data, and pre- and post-treatment thyroid stimulating hormone (TSH) levels were obtained for patients diagnosed with ES-LSCC from 2008-2012. Inclusion criteria consisted of patients diagnosed clinically with ES-LSCC (T1 or 2, N0, M0) treated with curative intent. Patients were excluded if there was a history of hypothyroidism before the treatment or any previous history of head and neck cancers. RESULTS: Ninety-five patients were included in this study. Mean age was 66.1 years (range: 44.0-88.0 years) and 82.3% of patients were male. Glottis was the most common subsite at 77.9% and the average follow-up was 40 months (Range: 12-56 months). Five-year overall survival generated using the Kaplan-Meier method was 79%. Incidence of hypothyroidism after RT was found to be 46.9%. The greatest frequency of developing hypothyroidism was at 12 months. CONCLUSIONS: We found a high prevalence of hypothyroidism for ES-LSCC treated with RT, with the highest rate at 12 months. Consequently, we recommend possible routine screening for hypothyroidism using TSH level starting at 12 months. To our knowledge, this is the first study to suggest the optimal timing for the detection of hypothyroidism.
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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 | 0.003 |
| 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 | 0.000 |
| 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".