The utility of parathyroid autofluorescence as an adjunct in thyroid and parathyroid surgery 2023
Bibliographic record
Abstract
Thyroid and parathyroid surgery requires careful dissection around the vascular pedicle of the parathyroid glands to avoid excessive manipulation of the tissues. If the blood supply to the parathyroid glands is disrupted, or the glands are inadvertently removed, temporary and/or permanent hypocalcemia can occur, requiring post-operative exogenous calcium and vitamin D analogues to maintain stable levels. This can have a significant impact on the quality of life of patients, particularly if it results in permanent hypocalcemia. For over a decade, parathyroid tissue has been noted to have unique intrinsic properties known as "fluorophores," which fluoresce when excited by an external light source. As a result, parathyroid autofluorescence has emerged as an intra-operative technique to help with identification of parathyroid glands and to supplement direct visualization during thyroidectomy and parathyroidectomy. Due to the growing body of literature surrounding Near Infrared Autofluorescence (NIRAF), we sought to review the value of using autofluorescence technology for parathyroid detection during thyroid and parathyroid surgery. A literature review of parathyroid autofluorescence was performed using PubMED. Based on the reviewed literature and expert surgeons' opinions who have used this technology, recommendations were made. We discuss the current available technologies (image vs. probe approach) as well as their limitations. We also capture the opinions and recommendations of international high-volume endocrine surgeons and whether this technology is of value as an intraoperative adjunct. The utility and value of this technology seems promising and needs to be further defined in different scenarios involving surgeon experience and different patient populations and conditions.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| 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".