Risk factors for hypocalcemia in dialysis patients with refractory secondary hyperparathyroidism after parathyroidectomy: a meta-analysis
Bibliographic record
Abstract
Hypocalcemia after parathyroidectomy (PTX) results in tetany, diarrhea, cardiac arrhythmia, and even sudden death. However, a meta-analysis or systematic evaluation of risk factors with the occurrence and development of hypocalcemia in patients with secondary hyperparathyroidism (SHPT) after PTX has never been performed. A thorough search of electronic databases, including PubMed, Web of Science, the Cochrane Library, and EMBASE, was performed to retrieve relevant studies from database inception to June 2021. Quality of the included studies was assessed by two independent reviewers using the Newcastle–Ottawa Scale. Review Manager 5.3 and Stata 16.0 were used for meta-analysis. The random-effects model was adopted to calculate the 95% CIs (I2> 50% or p < 0.05) of the combined effect size and the corresponding homogeneous data. Otherwise, a fixed-effects model was used. Thirteen studies including 2990 participants who met the inclusion criteria were enrolled in the present meta-analysis. The overall quality of the enrolled studies had a score of >7 points. Risk factors significantly related to hypocalcemia in patients with SHPT after PTX were preoperative serum calcium (OR 0.19, 95%CI 0.11–0.31), preoperative alkaline phosphatase (ALP) (OR 1.01, 95% CI 1.01–1.02), and preoperative intact parathyroid hormone (iPTH) (OR 1.38, 95%CI 1.20–1.58). Meanwhile, age (OR 0.97, 95%CI 0.87–1.10) was not significantly correlated with hypocalcemia after PTX. Based on the current evidence, preoperative serum calcium, preoperative ALP, and preoperative iPTH were significant predictors of hypocalcemia in patients with SHPT after PTX. More attention should be given to patients with these risk factors for the prevention of postoperative hypocalcemia.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.056 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".