Intraoperative parathyroid hormone monitoring criteria in secondary and tertiary hyperparathyroidism: A systematic review
Bibliographic record
Abstract
• Studies of intraoperative parathyroid hormone (IOPTH) criteria in secondary and tertiary hyperparathyroidism are poor quality and preclude meta-analysis • Based on the current literature, we cannot recommend IOPTH criteria for secondary of tertiary hyperparathyroidism • Future high-quality studies are needed to evaluate the best IOPTH criteria for secondary and tertiary hyperparathyroidism There are no recommendations regarding optimal use of intraoperative parathyroid hormone (IOPTH) to guide surgery for secondary (SHPT) or tertiary (THPT) hyperparathyroidism. We performed a systematic review to evaluate the diagnostic performance of IOPTH criteria in SHPT and THPT. We performed a library search for primary research articles published from 1990 to 2024 that evaluated any IOPTH criteria in SHPT and THPT. We were unable to proceed with diagnostic test accuracy network meta-analysis due to low study quality, so we performed a descriptive analysis of diagnostic properties [e.g., positive predictive value (PPV) and negative predictive value (NPV)] for IOPTH criteria. Thirty-seven articles met inclusion criteria. Poor reporting quality amongst eligible prevented us from performing our planned diagnostic test accuracy network meta-analysis. Most studies (56.8%) investigated SHPT with follow-up ranging from 1–48 months. Most studies investigated Miami or modified Miami IOPTH criteria, but over 50% of studies did not report the reference standard. Present-day IOPTH criteria used in surgery for SHPT and THPT appeared to maintain the PPV, but the NPV ranged from 20.8%–70.6% in SHPT and 22.2%–50% in THPT. The poor quality of renal hyperparathyroidism research precludes pooled analysis to evaluate the diagnostic performance of IOPTH criteria in patients diagnosed with SHPT or THPT. Current IOPTH criteria may be unhelpful in these patients; hence, surgeons should continue to investigate methods and criteria for optimizing IOPTH during surgery for SHPT and THPT.
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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 | 0.010 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.014 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".