Ultrasonographic Diagnosis of Thyroid Papillary Carcinoma Metastasis
Bibliographic record
Abstract
This study selected the medical record data of patients who underwent ultrasound examination and surgical treatment at Shaw Hospital, affiliated with Zhejiang University Medical College, from January 2018 to June 2020 and screened nine high-risk patients with thyroid papillary carcinoma and eight low-risk patients who met the enrollment criteria.The pathological and ultrasonic imaging data of the thyroid nodules and cervical lymph nodes of the enrolled patients were retrospectively analyzed.Clinicopathological aspects: age, number of lesions, lymph node area, and number of lymph nodes were significant.The younger the age, the higher the risk of cancer metastasis; the more the lymph node metastasis areas, the greater the number of lesions, and the higher the risk of cancer metastasis.An ultrasound image analysis of thyroid nodules showed that the number of microcalcifications and lesions was significantly and positively correlated with the risk of cancer metastasis.Ultrasonic image analysis of cervical lymph nodes showed that the indicators of cystic change, the number of calcifications, number of lymph node fusions, and number of clear lymph node boundaries were significantly and positively correlated with the risk of cancer metastasis.This study is crucial in the clinical diagnosis of thyroid papillary carcinoma metastasis and provides a theoretical basis for surgeons to formulate diagnosis and treatment plans for thyroid carcinoma.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".