Intraoperative parathyroid hormone monitoring in parathyroidectomy for hyperparathyroidism: a protocol for a network meta-analysis of diagnostic test accuracy
Bibliographic record
Abstract
Intraoperative parathyroid hormone (iPTH) monitoring is standard-of-care in the surgical management of hyperparathyroidism. It involves real-time determination of circulating PTH levels to guide parathyroid gland excision. There exists several iPTH monitoring criteria, such as the Miami criteria, and a lack of standardization in the timing of post-parathyroid gland excision samples. We present a protocol of a systematic review and network meta-analysis of diagnostic test accuracy to identify the iPTH criteria and post-gland excision timepoint that best predicts surgical cure in hyperparathyroidism. The database search strategy will be developed in conjunction with a librarian specialist. We will perform a search of Medline (Ovid), EMBASE (Ovid), CINAHL, Cochrane Collaboration, and Web of Science from 1990–present. Studies will be eligible if they include adult patients diagnosed with hyperparathyroidism who undergo parathyroidectomy with iPTH monitoring. We will only include studies that report diagnostic test properties for iPTH criteria and/or post-excision sampling timepoints. All screening, full-text review, data extraction, and critical appraisal will be performed in duplicate. Critical appraisal will be performed using QUADAS-2 instrument. A descriptive analysis will present study and critical appraisal characteristics. We will perform evaluation of between-study heterogeneity using I2 and Cochrane Q and where applicable, we will perform sensitivity analysis. Our network meta-analysis will include Bayesian hierarchical framework with random effects using multiple models. Ethics approval is not required. This proposed systematic review will utilize a novel Bayesian network meta-analysis model to help standardize iPTH monitoring in hyperparathyroidism, thereby optimizing patient outcomes and healthcare expenditures.
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 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 | 0.079 | 0.172 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.030 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.045 | 0.005 |
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".