Cine clips increase the detection of thyroid pyramidal lobe in routine thyroid sonogram
Bibliographic record
Abstract
Background: Identification and resection of the thyroid pyramidal lobe is important for thyroid cancer surgery in order to prevent interval cancer in residual thyroid tissue. Purpose: The purpose of this study was to determine how often a thyroid pyramidal lobe is found in patients with and without previous thyroidectomy and to optimise the protocol for identifying thyroid pyramidal lobes during routine thyroid ultrasonography. Material and Methods: In this prospective study, a total of 1579 patients who received routine thyroid ultrasound scans at a single centre were enrolled. A dedicated standard scanning protocol was established containing both static images of the anterior neck superior to the thyroid as well as a transverse cine loop starting from the isthmus to the hyoid bone. The presence and features of thyroid pyramidal lobes were evaluated and compared. Results: Detection rate of thyroid pyramidal lobes in patients without thyroidectomy improved from 39.5% (480/1215) to 49.7% (640/1215) with protocol adding cine-loop as compared to protocol without cine-loop. The cine-loops were particularly helpful in the detection of thyroid pyramidal lobes when it is separated from the main lobe or in thyroidectomy patients. By adding assessment with cine-loop into the dedicated protocol, we have further detected different pathologies occurring on thyroid pyramidal lobes including benign and malignant solid nodules and pseudo-nodules of Hashimoto's thyroiditis. Conclusion: The addition of dynamic assessment with cine-loop increases the detection rate of thyroid pyramidal lobes. By paying attention to the thyroid pyramidal lobe in pre-operative diagnostic sonographic images, we can help to avoid incomplete removal of the thyroid gland during thyroidectomy.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".