Systematic review and meta-analysis of predictors of post-thyroidectomy hypocalcaemia
Bibliographic record
Abstract
BACKGROUND: Hypocalcaemia is common after thyroidectomy. Accurate prediction and appropriate management may help reduce morbidity and hospital stay. The aim of this study was to perform a systematic literature review and meta-analysis of predictors of post-thyroidectomy hypocalcaemia. METHODS: A systematic search of PubMed, EMBASE and the Cochrane Library databases was undertaken, and the quality of manuscripts assessed using a modified Newcastle-Ottawa Scale. RESULTS: Some 115 observational studies were included. The median (i.q.r.) incidence of transient and permanent hypocalcaemia was 27 (19-38) and 1 (0-3) per cent respectively. Independent predictors of transient hypocalcaemia included levels of preoperative calcium, perioperative parathyroid hormone (PTH), preoperative 25-hydroxyvitamin D and postoperative magnesium. Clinical predictors included surgery for recurrent goitre and reoperation for bleeding. A calcium level lower than 1·88 mmol/l at 24 h after surgery, identification of fewer than two parathyroid glands (PTGs) at surgery, reoperation for bleeding, Graves' disease and heavier thyroid specimens were identified as independent predictors of permanent hypocalcaemia in multivariable analysis. Factors associated with transient hypocalcaemia in meta-analyses were inadvertent PTG excision (odds ratio (OR) 1·90, 95 per cent confidence interval 1·31 to 2·74), PTG autotransplantation (OR 2·03, 1·44 to 2·86), Graves' disease (OR 1·75, 1·34 to 2·28) and female sex (OR 2·28, 1·53 to 3·40). CONCLUSION: Perioperative PTH, preoperative vitamin D and postoperative changes in calcium are biochemical predictors of post-thyroidectomy hypocalcaemia. Clinical predictors include female sex, Graves' disease, need for parathyroid autotransplantation and inadvertent excision of PTGs.
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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 | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.035 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".