Early Prediction of Hypocalcemia after Thyroidectomy using Parathyroid Hormone: An Analysis of Pooled Individual Patient Data from Nine Observational Studies
Bibliographic record
Abstract
BACKGROUND: Monitoring for hypocalcemia after thyroidectomy, using only symptoms and serum calcium levels, can delay the discharge of patients who will remain normocalcemic and can delay the treatment of hypocalcemic patients. STUDY DESIGN: We conducted a systematic search for articles describing use of parathyroid hormone (PTH) assay, checked within hours of completing thyroidectomy, to predict postoperative symptomatic hypocalcemia. Studies were excluded if all patients were treated with postoperative calcium, or if early PTH values were used to alter management of the patient. Individual patient data (perioperative PTH and calcium levels, development of hypocalcemia) were obtained for 457 patients from the corresponding authors of 9 studies and pooled to yield the following results. RESULTS: PTH, checked at three time periods after removal of the thyroid gland (0 to 20 minutes, 1 to 2 hours, and 6 hours), was substantially lower in patients who became hypocalcemic compared with those who remained normocalcemic. The accuracy of PTH in determining hypocalcemia increased with time and was excellent when checked 1 to 6 hours postoperatively. A single PTH threshold (65% decrease compared with preoperative level), checked 6 hours after completing thyroidectomy, had a sensitivity of 96.4% and specificity of 91.4% in detecting postoperative hypocalcemia. CONCLUSIONS: PTH assay, when checked 1 to 6 hours after thyroidectomy, has excellent accuracy in determining which patients will become symptomatically hypocalcemic. Routine use of this assay should be considered because it may allow earlier discharge of the normocalcemic patient and earlier identification of patients requiring treatment of postthyroidectomy 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 | 0.024 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.016 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".