Management of recurrent and persistent metastatic lymph nodes in well‐differentiated thyroid cancer: A multifactorial decision‐making guide for the thyroid cancer care collaborative
Bibliographic record
Abstract
BACKGROUND: Well-differentiated thyroid cancer (WDTC) recurs in up to 30% of patients. Guidelines from the American Thyroid Association (ATA) and the National Comprehensive Cancer Network (NCCN) provide valuable parameters for the management of recurrent disease, but fail to guide the clinician as to the multitude of factors that should be taken into account. The Thyroid Cancer Care Collaborative (TCCC) is a web-based repository of a patient's clinical information. Ten clinical decision-making modules (CDMMs) process this information and display individualized treatment recommendations. METHODS: We conducted a review of the literature and analysis of the management of patients with recurrent/persistent WDTC. RESULTS: Surgery remains the most common treatment in recurrent/persistent WDTC and can be performed with limited morbidity in experienced hands. However, careful observation may be the recommended course in select patients. Reoperation yields biochemical remission rates between 21% and 66%. There is a reported 1.2% incidence of permanent unexpected nerve paralysis and a 3.5% incidence of permanent hypoparathyroidism. External beam radiotherapy and percutaneous ethanol ablation have been reported as therapeutic alternatives. Radioactive iodine as a primary therapy has been reported previously for metastatic lymph nodes, but is currently advocated by the ATA as an adjuvant to surgery. CONCLUSION: The management of recurrent lymph nodes is a multifactorial decision and is best determined by a multidisciplinary team. The CDMMs allow for easy adoption of contemporary knowledge, making this information accessible to both patient and clinician.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".