Seasonal Difference in Postthyroidectomy Hypocalcemia
Bibliographic record
Abstract
OBJECTIVE: Hypocalcemia following thyroidectomy often prolongs hospital stay and is potentially life-threatening. The objective of this study is to determine whether the season when thyroidectomy is performed is associated with postoperative hypocalcemia. STUDY DESIGN: Retrospective case series of patients undergoing thyroid surgery from 2009 to 2015. SETTING: Tertiary care academic institution in Montreal, Canada. SUBJECTS AND METHODS: A consecutive sample of 823 patients undergoing thyroidectomy by a single high-volume otolaryngologist for a suspected or confirmed thyroid malignancy. Patient demographics, procedure type, calcium and vitamin D supplementation, and seasonal rate of hypocalcemia postthyroidectomy were calculated and compared. RESULTS: Average seasonal rates of postthyroidectomy hypocalcemia in the winter, spring, summer, and autumn were, respectively, 8.3% (8 of 216), 7.3% (12 of 165), 1.5% (3 of 201), and 3.5% (8 of 228; P < .005). Patients operated in the winter were 5.6 times more likely to develop hypocalcemia as compared with those in the summer (P < .01; 95% confidence interval: 1.7-18.7). In a multiple regression analysis factoring in season when surgery was performed, procedure type, and preoperative vitamin D/calcium supplementation, surgery occurring in the winter predicted a hypocalcemia event (correlation coefficient [SE]: 0.72 [0.024], P = .026; 0.006 [0.025], P = .81; 0.004 [0.019], P = .82, respectively). CONCLUSION: In this study, patients undergoing thyroidectomy in the winter months were more likely to develop postoperative hypocalcemia when compared with those operated in the summer. Further studies are needed to understand the role of vitamin D in the observed seasonal difference in hypocalcemia rates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".