Total parathyroidectomy with autotransplantation versus subtotal parathyroidectomy for renal hyperparathyroidism: A systematic review and meta‐analysis
Bibliographic record
Abstract
AIM: Total parathyroidectomy with autotransplantation (TPTX + AT) and subtotal parathyroidectomy (SPTX) have been recommended to patients with renal hyperparathyroidism (RHPT).But which one is the best surgical method remains controversial. The aim of the present study was to compare the two surgical procedures with respect to long-term outcomes. METHODS: A literature search was undertaken using Medline, EMBASE, CNKI and CBM from inception to May 2015. Study quality was assessed using the Newcastle-Ottawa Scale. Data were analyzed using Review Manager version 5.1.0. RESULTS: A total of 13 studies comprising 1589 patients with renal failure were identified. There was no statistically significant difference in the rate of symptomatic improvement (OR 0.77; 95%CI 0.22 to 2.69; P = 0.68), radiological success (OR 0.17; 95%CI 0.02 to 1.56; P = 0.90), hyperparathyroidism recurrence or persistence (OR 1.31; 95%CI 0.65 to 2.65; P = 0.45) and reoperation (OR 1.55; 95%CI 0.62 to 3.86; P = 0.35) between TPTX + AT and SPTX. The effects on serum calcium and parathyroid hormone (PTH) were similar between two surgical protocols. CONCLUSION: Both the TPTX + AT and SPTX were effective in treating RHPT and preventing recurrence. The difference between the two surgeries in recurrence or persistence and reoperation rate was insignificant. Further prospective, randomized controlled trials with high statistic power are necessary to comparative the two surgeries on the long term safety.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.023 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".