Biomarker panel diagnosis of thyroid cancer: a critical review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The accurate preoperative diagnosis of thyroid cancer continues to be a significant challenge for those individuals who present with nodular thyroid disease, particularly for tumors with indeterminate cytomorphological features by fine-needle aspiration biopsy. In an effort to develop improved diagnostic tools, a number of studies have investigated the discriminatory potential of many different RNA and protein molecules. However, no individual thyroid cancer biomarker has been found with sufficient sensitivity and specificity. Therefore, research focus has shifted to panels of multiple markers with the hope of improved performance and robustness. A panel comprised of GAL3, CK19 and HBME1 is by far the most studied to date and offers some improvement over individual marker performance alone. However, relatively few marker panels have been studied and their performances and application as diagnostic tests have not been consistently reported. We present a comprehensive review of molecular marker panel studies for thyroid tumors and current issues and challenges. In the future, studies evaluating larger numbers of biomarkers in large patient cohorts are required for the development and validation of a clinically applicable test.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it