A Meta-Analysis of Risk Factors for Transient and Permanent Hypocalcemia After Total Thyroidectomy
Bibliographic record
Abstract
BACKGROUND: As hypocalcemia is the most common complication of total thyroidectomy, identifying its risk factors should guide prevention and management. The purpose of this study was to determine the risk factors for postthyroidectomy hypocalcemia. METHODS: We searched PubMed, Web of Science and EMBASE through January 31, 2019, and assessed study quality using the Newcastle-Ottawa Scale. RESULTS: < 0.05) predictors of transient hypocalcemia were: younger age, female, parathyroid autotransplantation (PA), inadvertent parathyroid excision (IPE), Graves' disease (GD), thyroid cancer, central lymph node dissection, preoperative severe Vitamin D deficiency, preoperative Vitamin D deficiency and a lower postoperative 24 h parathyroid hormone (PTH) level. Preoperative magnesium, preoperative PTH and Hashimoto's thyroiditis were not significant predictors of transient hypocalcemia. IPE, GD, and thyroid cancer were associated with an increased rate of permanent hypocalcemia, but gender and PA did not predict permanent hypocalcemia. CONCLUSION: Important risk factors for transient and permanent hypocalcemia were identified. However, given the limited sample size and heterogeneity of this meta-analysis, further studies are required to confirm our preliminary findings.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.026 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".