Body Mass Index in the Evaluation of Thyroid Cancer Risk
Bibliographic record
Abstract
BACKGROUND: Obesity has been linked to numerous diseases including thyroid cancer, but the exact nature of the relationship, especially with respect to patients with thyroid nodules, remains unclear. The objective of this study was to evaluate the impact of body mass index (BMI) on thyroid cancer risk in a population of patients with indeterminate cytology on fine-needle aspiration biopsy (FNAB). METHODS: A total of 253 consecutive patients with indeterminate thyroid nodule FNABs who underwent total thyroidectomy in a tertiary care teaching hospital between 2002 and 2007 were reviewed. Height and weight reported on the anesthesia summary were recorded for each patient. Malignancy rates were calculated for the underweight, normal, overweight, and obese groups stratified according to their BMI. Subanalyses according to age and sex were also performed. RESULTS: The risk of malignancy tended to be lower in obese patients compared to patients with BMIs in the underweight, normal, and overweight ranges (52% vs. 61%, p = 0.195). In men, a BMI classified as obese was associated with a significantly lower rate of malignancy (36% vs. 72%, p = 0.003). Women older than 45 years were a subgroup in which higher malignancy rates were associated with obesity (65% vs. 54%, p = 0.293). Conversely, in men over the age of 45 years and women under 45 years, a BMI in the obesity range was linked to a lower incidence of malignancy (20% vs. 68% p = 0.009 and 36% vs. 68% p = 0.043, respectively). When older women were excluded from the population studied, the rate of malignancy in obese patients was 36% versus 70% in nonobese patients (p = 0.002) with an associated reduction of 5% in the risk of malignancy per increased unit of BMI. CONCLUSIONS: For patients with FNAB results of indeterminate significance, a higher BMI correlates with lower rates of thyroid malignancy for all patients except women over the age of 45 years.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".