Risk factors of perioperative hypoparathyroidism after thyroidectomy: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Perioperative hypoparathyroidism (hypoPT) represents a prevalent complication of thyroid surgery. Reported risk factors remain inconsistent, and identical factors may exert different effects depending on whether hypoPT is defined by serum calcium concentration or parathyroid hormone (PTH) levels. A comprehensive synthesis was undertaken to clarify risk factors for hypoPT under these distinct biochemical definitions. MATERIALS AND METHODS: Three databases (PubMed, Embase, and Scopus) were searched from inception to 2025. Study quality was assessed using the Newcastle-Ottawa scale. Pooled odds ratios (ORs) were calculated to examine associations between risk factors and perioperative hypoPT defined by calcium or PTH. Subgroup analyses were performed according to biochemical definitions, and publication bias was assessed with Begg and Egger tests. RESULTS: Sixty-four studies reporting 19 risk factors were included. Sex was the most frequently analyzed variable. For hypoPT defined by calcium, significant associations were observed with female sex, parathyroid glands remaining in situ , central neck dissection, lateral neck dissection, malignant pathology, parathyroid autotransplantation, incidental parathyroidectomy, parathyroid tissue in the specimen, and type of surgery (total thyroidectomy vs partial thyroidectomy). Higher postoperative PTH levels acted as a protective factor for calcium-defined hypoPT. For PTH-defined hypoPT, only malignant pathology showed a statistically significant association. CONCLUSION: Patients with thyroid cancer undergoing total thyroidectomy with lateral neck dissection are at greatest risk of perioperative hypoparathyroidism. Risk reduction relies on precise intraoperative identification of the parathyroid glands, improved surgical proficiency and awareness, and prompt correction of inadvertent excision.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".