External Beam Radiation in Differentiated Thyroid Carcinoma
Bibliographic record
Abstract
The treatment of differentiated thyroid carcinoma (DTC) is surgery followed in some cases by adjuvant treatment, mostly with radioactive iodine (RAI). External beam radiotherapy (EBRT) is less common and not a well-established treatment modality in DTC. The risk of recurrence depends on three major prognostic factors: extra-thyroid extension, patient's age, and tumor with reduced iodine uptake. Increased risk for recurrence is a major factor in the decision whether to treat the patient with EBRT. Data about the use of EBRT in DTC are limited to small retrospective studies. Most series have demonstrated an increase in loco-regional control. The risk/benefit from giving EBRT requires careful patient selection. Different scoring systems have been proposed by different investigators and centers. The authors encourage clinicians treating DTC to become familiarized with those scoring systems and to use them in the management of different cases. The irradiated volume should include areas of risk for microscopic disease. Determining those areas in each case can be difficult and requires detailed knowledge of the surgery and pathological results, and also understanding of the disease-spreading pattern. Treatment with EBRT in DTC can be beneficial, and data support the use of EBRT in high-risk patients. Randomized controlled trials are needed for better confirmation of the role of EBRT.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".