Timing of intraoperative baseline parathormone estimation at the time of parathyroidectomy.
Bibliographic record
Abstract
The objectives of this study were two-fold, firstly to determine the best tool to confirm completion of resection of all hypercellular parathyroid tissue. Secondly, to assess the optimal time for drawing baseline parathyroid hormone (PTH) sample on the day of surgery. 190 consecutive patients who underwent parathyroidectomy for sporadic primary hyperparathyroidism at a single tertiary care center between January 1, 2008 and May 31, 2012 were included in this largely retrospective study. Conventionally, a single baseline intraoperative PTH (ioPTH) measurement is collected; however, in a subset of these patients, we opted to collect two baseline samples in order to strengthen the data and create matched pairs. As part of the prospective arm of this study, 30 patients had both pre- and post-induction ioPTH levels measured; their mean PTH level pre-induction was 202 ng/L and mean PTH level post-induction was 292 ng/L. A paired-samples t-test demonstrated a statistically significant difference between the two means (p-value = 0.045). Mean percent change in ioPTH level from baseline to post-excision sample was also evaluated. Mean percent change in PTH from pre-induction to post-excision was 59.4% and from post-induction to post-excision was 68.0%. A paired-samples t-test demonstrated a statistically significant difference between the two means (p-value = 0.032). The clinical implication of these results is rooted in surgical decision-making. In 4 of the 30 cases, the Miami criterion was satisfied only with the post-induction measurement as baseline; if the pre-induction PTH measurement had served as a baseline, surgery would’ve continued needlessly.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".