Use of radioiodine‐131 scan to measure influence of surgical discipline, practice, and volume on residual thyroid tissue after total thyroidectomy for differentiated thyroid carcinoma
Bibliographic record
Abstract
BACKGROUND: Our study's purpose is to determine the influence of surgical discipline, surgeon site, and volume on remnant thyroid tissue visualized on radioactive iodine-131 (I-131) scans after total thyroidectomy and I-131 ablation in patients with well-differentiated thyroid carcinomas. METHODS: We retrospectively reviewed all cases of patients who received I-131 therapeutic ablation and postablation radioactive I-131 scans at our center after thyroidectomy to calculate the fraction of administered dose multiplied by 1000 (UDR1000). RESULTS: The remnant thyroid tissue (ie, the UDR1000), between academic and community surgeons was 0.471 (±0.705) and 1.190 (±2.487), respectively (P = .001). The UDR1000 between otolaryngology-head and neck surgery and general surgery was 0.654 (±1.575) and 1.043 (±1.625), respectively (P = .159). The UDR1000 partitioned by patient frequencies of <10, 10 to 19, and ≥20 patients yielded 1.255 (±2.554), 0.926 (±2.084), and 0.467 (±0.721), respectively (P = .003). CONCLUSION: Our study found statistically significant differences in residual thyroid tissue visualized on radioactive I-131 scans based on surgeon parameters.
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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 | 0.001 | 0.005 |
| 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) | 0.001 | 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".