AB091. A systematic review and meta-analysis comparing the safety and efficacy of minimally invasive parathyroidectomy with intra operative PTH versus post operative PTH for primary hyperparathyroidism
Bibliographic record
Abstract
Background: Intra-operative parathyroid hormone (ioPTH) assays may increase cure rates in patients undergoing attempted minimally invasive parathyroidectomy (MIP) for primary hyperparathyroidism (PHP) as compared to MIP with post-operative parathyroid hormone (poPTH) measurement only Methods: A systematic search was performed to identify randomised control trials and observational studies that compared MIP with and without ioPTH. Dichotomous variables were pooled as odds ratios (OR) while continuous variables were compared using weighted mean differences (WMD). Quality assessment was performed using the Newcastle-Ottawa (NOS) scale. Results: A total of 12 studies, involving 2,290 patients with PHP, (ioPTH n=1,148, poPTH n=1,132) were eligible for inclusion. The studies had a moderate to high risk of bias. The median NOS was 7 (range, 6–8). MIP patients who had ioPTH monitoring had increased cure rates (cure RR 4.40, 95% CI: 2.12–7.10, P<0.0001). There was an increased need for reoperation in the poPTH group (RR 2.32, 95% CI: 0.19–0.86, P=0.02). There was a trend towards increased operating times in the ioPTH group, however this did not reach statistical significance (WMD 1.88, 95% CI: −0.93–44.17, P=0.06). The use of ioPTH lead to increased rates of bilateral neck exploration (BNE) (RR 2.41, 95% CI: 1.27–9.92, P=0.02). Conclusions: IoPTH testing improves cure rates for patients with PHP undergoing attempted MIP. Attempted MIP without ioPTH is associated with less BNE with a trend towards shorter operating times but results in lower cure rates and increased risk for re-operation.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.030 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".