Trends in using intraoperative parathyroid monitoring during parathyroidectomy: Protocol and rationale for a cross-sectional survey study of North American surgeons
Bibliographic record
Abstract
ABSTRACT Introduction Hyperparathyroidism is a common endocrine disorder that can be secondary to a single or multiple abnormal parathyroid glands and can occur in the context of chronic kidney disease (CKD). There are three types of hyperparathyroidism, and all are definitively managed via surgical extirpation of abnormal parathyroid gland tissue. Intraoperative parathyroid hormone (IOPTH) monitoring was introduced over three decades ago and has been shown to improve clinical outcomes in patients with primary hyperparathyroidism (PHPT). As the incidence of PHPT rises due to improving screening globally and the incidence of CKD rises, it will be important to optimize adoption and standardization of IOPTH within endocrine surgery centers around the world. We will perform a cross-sectional survey study of surgeon rationale, operational details, and barriers associated with IOPTH adoption across North America. Methods and analysis We will utilize a convenience sampling technique to distribute an online survey to head and neck surgeons and endocrine surgeons across North America. This survey will be distributed via email to three North American professional societies (i.e., Canadian Society for Otolaryngologists–Head and Neck Surgeons, American Head and Neck Society, and American Association of Endocrine Surgeons). The survey will consist of 30 multiple choice questions that are divided into three concepts: (1) participant demographics and training details, (2) details of surgical adjuncts during parathyroidectomy, and (3) barriers to adoption of IOPTH. Descriptive analyses and multiple logistic regression models will be used to evaluate the impact of demographic, institutional, and training variables on the use of IOPTH monitoring and barriers to IOPTH adoption. Discussion This study will explore IOPTH monitoring for guiding parathyroid surgeries in secondary and tertiary hyperparathyroidism. An ability to capture surgeon practices regarding IOPTH monitoring will inform trials aimed to help optimize IOPTH in challenging populations. Ethics and dissemination Ethics approval was obtained by the Hamilton Integrated Research Ethics Board (2024-17173-GRA). We do not expect any survey respondents to experience any harms because of participating in this study. We plan to present the results of this study at national and international conferences, and we will publish these findings in peer-reviewed surgical journals. We plan to use these study findings to advocate for adoption of IOPTH technologies and inform future studies and trials.
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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 | 0.028 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".