The relevance of preoperative ultrasound cervical mapping in patients with thyroid cancer
Bibliographic record
Abstract
BACKGROUND: Cervical lymph node involvement in thyroid cancer is associated with locoregional recurrence and decreased disease-free survival. Preoperative lymph node mapping helps in planning surgery for neck dissection and improves patient outcomes. We sought to perform a qualitative and quantitative analysis of ultrasound mapping for thyroid cancer and evaluate the clinical importance of this exam in terms of identifying the group of patients who would benefit most from subsequent surgical dissection. METHODS: We retrospectively reviewed the cases of 263 patients who underwent thyroid surgery between 2009 and 2013. We calculated the positive predictive values (PPVs) of ultrasound mapping of both the lateral and central compartments together and the lateral or central compartment individually. A quantitative analysis was performed by comparing the number of positive lymph nodes at ultrasound imaging with histopathologic evaluation. RESULTS: A total of 136 cases of thyroid cancer in 120 patients met the inclusion criteria for ultrasound mapping analysis. The PPVs (and 95% confidence intervals) were 83.82 (0.76-0.89) for the lateral and central compartments, 85.39% (0.76-0.91) for the lateral compartment, and 80.48% (0.7-0.87) for the central compartment. When comparing the positive lymph nodes at ultrasound imaging with histopathologic evaluation, the result was χ(2) = 10.33 (p = 0.006). CONCLUSION: This single-institution study indicated that preoperative ultrasound mapping is an accurate imaging procedure for predicting lymphatic spread in differentiated and medullary thyroid cancer. Ultrasound mapping can be used as an efficient tool for surgical planning and prognosis determination, as well as for identifying the group of patients who would benefit most from subsequent surgical intervention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 |
| 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 | 0.000 |
| 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".