Postoperative Parathyroid Hormone Level as a Predictor of Post-thyroidectomy Hypocalcemia
Bibliographic record
Abstract
OBJECTIVES: To evaluate levels of parathyroid hormone following total thyroidectomy in order to ascertain its ability to predict postoperative hypocalcemia. To establish standardized criteria permitting the safe discharge of total thyroidectomy patients within 13 hours of surgery. METHODS: This is a prospective study in which parathyroid hormone levels were tested in 54 consecutive patients who underwent total thyroidectomy. Levels were measured postoperatively at 6, 12, and 20 hours. Corrected calcium levels were also measured at 6, 12, and 20 hours in accordance with the preexisting protocol. RESULTS: Statistical analysis demonstrates that patients with corrected calcium levels greater than or equal to 2.14 mmol/L and parathyroid hormone levels greater than or equal to 28 ng/L at 12 hours post-thyroidectomy can be discharged without further need for calcium monitoring. The analysis also demonstrates that patients with 12-hour parathyroid hormone levels less than or equal to 20 ng/L are at significant risk of developing hypocalcemia. CONCLUSION: Parathyroid hormone levels in conjunction with corrected calcium values are accurate predictors of the calcium trends of post-thyroidectomy patients. Implementation of this protocol can result in shorter hospital stays for the majority of post-thyroidectomy patients, which can translate into substantial cost savings for the health care system.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".