Triple diagnostic test in the evaluation of thyroid nodules
Bibliographic record
Abstract
Background: Thyroid nodules are a common endocrine disease whose prevalence in India is approximately 12.2%. Although most patients with suspected nodules have benign conditions, the overestimation of malignancy leads to the performance of unnecessary procedures. No clinical, radiological and cytological parameters has singularly shown significant impact on clinical practice and post-operative histopathological examination remains the gold standard in the diagnosis of malignancy.Methods: 55 patients with thyroid nodules were evaluated and the Clinical assessment findings were recorded by McGill thyroid nodule score, ultrasonography findings using TIRADS and FNAC findings by the Bethesda system. The triple test was then used to classify them and these results were compared with the HPE of the post-operative specimen.Results: The sensitivity and specificity of TIRADS, FNAC were higher as compared to clinical score; clinical score had lowest sensitivity of 72.73%. The sensitivity, specificity, PPV, NPV and accuracy of triple test was 100%. Triple test had higher sensitivity, specificity and accuracy in differentiating thyroid nodules as compared to any of the three parameters used individually.Conclusions: Triple test has higher accuracy, sensitivity and specificity in determining the nature of thyroid nodule than each of the parameters used individually and it is especially useful in follicular lesions. On the basis of the results of this study, we conclude that the triple test can reliably be used to differentiate benign and malignant nodules preoperatively.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".