Are Patients Undergoing Thyroidectomy in Montreal, Canada, during Winter at Increased Risk for Postoperative Hypocalcemia?
Bibliographic record
Abstract
Objectives: (1) Review the rate of postoperative hypocalcemia in patients undergoing thyroidectomy in the summer and winter. (2) Identify the association between the season when surgery was completed and the risk of postoperative hypocalcemia. Methods: A retrospective chart review of 436 patients undergoing thyroidectomy at the McGill University Thyroid Cancer Centre from 2006 to 2014 was performed. Patients undergoing total or completion thyroidectomy in the winter months (December to February) and summer months (July to September) were included in the study. Parathyroid hormone (PTH) and serum corrected calcium were recorded according to the McGill post‐thyroidectomy protocol. Preoperative PTH and 25‐hydroxyvitamin D (25‐OHD) were measured. Hypocalcemia was defined as a corrected calcium level <1.9 mmol/L. Results: The rate of postoperative hypocalcemia was 8% for patients operated on in the winter and 1.8% for those in the summer (P =. 01). Patients undergoing surgery in the winter were 4.3 times more likely to develop postoperative hypocalcemia than those in the summer (P =. 01, 95% confidence interval [1.5 to 1 5]). Surgery during the summer months showed a higher preoperative 25‐OHD level (P =. 04). Preoperative PTH levels were significantly greater in the winter months as compared with the summer (5 pmol/L and 4.5 pmol/L, respectively; P =. 01). Patients with 25‐OHD deficiency (≤70nmol/L) were not found to have a higher rate of postoperative hypocalcemia (5.2% and 13.8%, respectively; P =. 22). Conclusions: In this study, patients undergoing thyroidectomy during the winter months were 4.3 times more likely to develop postoperative hypocalcemia when compared with patients operated in the summer.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".