What is the most appropriate intraoperative baseline parathormone? A prospective cohort study
Bibliographic record
Abstract
INTRODUCTION: The time of drawing pre-incision intraoperative parathyroid hormone (ioPTH) is crucial to provide the right baseline for post-excision PTH measurement. The objective of this study was to identify the optimal time and the numbers of baseline PTH samples that best predict excision of all hypercellular parathyroid tissue when compared with 10-min post-excision PTH level. MATERIALS AND METHODS: In this prospective study, two pre-incision (pre-induction and 10-min post-induction) baseline ioPTH samples along with pre- and post-excision ioPTH were collected and analyzed for 352 parathyroidectomies in 341 patients for sporadic primary hyperparathyroidism at a University hospital. Paired Wilcoxan signed rank test was used to compare the pre-incision ioPTH levels and their percent drop to 10-min post-excision levels. Sensitivity, specificity, predictive values and receiver operating characteristic (ROC) curves were used to compare the predictability of the two pre-incision levels. RESULTS: The difference between pre- and post-induction baseline PTH levels was highly significant (p < 0.001). In 4% cases the criterion of post-excision PTH drop of ≥50% was achieved only with the post-induction baseline PTH and not with pre-induction PTH measurement. Using pre-induction baseline, ioPTH had an overall accuracy of 90% whereas ≥50% fall in the post-excision PTH from the post-induction baseline PTH had the accuracy of 94.85%. DISCUSSION: There was a significant difference between pre- and post-induction PTH levels and Miami criteria was met in 95.45% cases with post-induction baseline. CONCLUSIONS: The optimal time for drawing pre-incision baseline PTH sample is at 10 min post-induction of general anesthesia and positioning of patient.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".