A prospective cohort study of intraoperative parathyroid gland management by early and advanced career surgeons based on viability assessment by visual perception and indocyanine green (ICG) fluorescence imaging
Bibliographic record
Abstract
Background Parathyroid gland identification and preservation during thyroidectomy has historically relied on experienced visual acumen. Indocyanine green fluorescence imaging has demonstrated improvement in parathyroid gland preservation, but it is unclear if the benefit is limited to less-experienced surgeons. The aim of this study was to identify how often discordant parathyroid viability assessments by surgeon visual perception versus indocyanine green fluorescence resulted in changes in management, stratified by surgeon years of experience. Methods Patients undergoing thyroid surgery by high-volume endocrine surgeons were recruited. Perception of parathyroid viability was documented before and after intravenous indocyanine green administration. In cases of discordant assessments, management was at the surgeon's discretion. Rates of discordance and change in management were stratified by surgeon experience—"early career" (<5 years independent practice) and "advanced career" (>15 years independent practice). Results were analyzed using the Pearson χ 2 test. The primary outcome was the rate of change in management of the parathyroid on the basis of discordant assessments stratified by surgeon years of experience. Results Thirty-five patients were included and comprised 81 observations of parathyroid viability, 50 by early career surgeons and 31 by advanced-career surgeons. Early career surgeons had a discordance rate of 26.0% ( n = 13/50) versus 19.3% ( n = 6/31) for advanced career surgeons (χ 2 = 0.17329, P = .6722). Of the 19 discordant observations, 13 (68.4%) resulted in changes in management (early career=10/13 = 76.9%; advanced career=3/6 = 50.0%; χ 2 = 1.3772, P = .3093). Conclusion These results suggest that indocyanine green fluorescence imaging is a useful adjunct to visual perception, regardless of the surgeon's years of experience.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".