Correlation of triple diagnostic (clinical, thyroid ultrasound, and FNAB) with histopathology in thyroid cancer patient
Bibliographic record
Abstract
Background: A thyroid nodule is a thyroid gland lesion that can be benign or malignant. The diagnosis was made based on clinical, radiological, and pathological findings, including McGill Thyroid Nodule Score (MTNS) for risk estimation, ultrasound using TIRADS criteria, and also fine needle aspiration biopsy (FNAB), which detected malignancy. However, the accuracy varied. The combination of these 3 diagnostic methods provides a better accuracy. This study aims to determine the correlation of triple diagnostics with histopathology in thyroid cancer patients. Method: This is a retrospective observational analytical study of thyroid nodule patients in Ulin Hospital from 2018 to 2022. Inclusion criteria in this study include patients with complete triple diagnostic data (clinical symptom, USG thyroid, and FNAB). Patients with incomplete data were excluded. Variables collected included demography, histopathology, symptom, ultrasonography (USG), and FNAB results. Data was analyzed with SPSS software using Spearman correlation and logistic regression tests. Results: A positive and statistically significant correlation between thyroid cancer with MTNS (r = 0.352, p = 0.002) and Bathesda (r = 0.240, p = 0.034) was discovered. A positive correlation was also found between TIRADS and thyroid cancer (r = 0.158) but not statistically significant (p>0,05). The combination of MTNS, TIRADS, and FNAB produced a strong positive and significant correlation (r = 0.510, p = 0.008) with thyroid cancer. Conclusion: MTNS and Bathesda categorization significantly correlated with thyroid cancer histopathology, while the TIRADS category presented a meaningless relationship. Triple diagnostics revealed a stronger and more significant correlation with histopathology in thyroid cancer patients.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".