Effect of autofluorescence monitoring on postoperative permanent hypoparathyroidism after total thyroidectomy
Bibliographic record
Abstract
Abstract Purpose: Post-operative hypoparathyroidism (POHP), permanent or temporary, is the commonest complication of thyroidectomy. To avoid hypoparathyroidism after thyroidectomy a few centers including ours have explored the use of parathyroid autofluorescent properties intra-operatively (AFI). The present supplementary study aimed to determine the rates of permanent POHP in patients undergoing total thyroidectomy (TT) 12 months after surgery and whether the introduction of AFI resulted in the reduction of its incidence. Methods: This was a supplementary prospective observational single-center study including the patients presenting postoperative temporary hypoparathyroidism after having undergone a scheduled TT and been randomly allocated into: (i) patients operated without near-infrared imaging (non-NIR group) and (ii) patients operated with near-infrared imaging (NIR group). These patients were re-evaluated, regarding albumin, 25-hydroxy-vitamin D, serum calcium, phosphorus, and PTH 12 months postoperatively. Results: In the NIR group were significantly fewer patients experiencing permanent POHP compared to the non-NIR group (0.00% versus 9.09%, p<0.001). Consequently, the level of PTH and serum total calcium were significantly lower in the non-NIR group 12 months after TT (p<0.001 and p=0.033 respectively). Conclusion: The ability of AFI to demonstrate parathyroid glands with high accuracy during TT decreases significantly the incidence of permanent POHP resulting in better outcomes after thyroid surgery.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".