Low Vitamin D and Risk of Post‐thyroidectomy Hypocalcemia
Bibliographic record
Abstract
Objective 1) Determine if low vitamin D reduces risk of post‐thyroidectomy hypocalcemia. 2) Verify if this is an independent effect. Method Retrospective study of 139 total thyroidectomy patients (October 2009‐October 2011) at a McGill University teaching hospital. Preoperative 25‐hydroxy‐vitamin D (25OHD), calcium, and PTH were measured. Patients were assessed for postoperative hypocalcemia. Low vitamin D (LVD) was defined as 25OHD ≤70 nmol/L (28 ng/mL) and optimal vitamin D (OVD) was defined as 25OHD >70 nmol/L (28 ng/mL). Results Hypocalcemia occurred in 3.2% (2 of 62) patients with LVD and 10.4% (8 of 77) patients with OVD (OR 0.2875, P =. 124). No patients with vitamin D deficiency (VDD), defined as 25OHD ≤35 nmol/L (14 ng/mL), developed hypocalcemia. Univariate analysis did not show age, sex, malignancy, presence of thyroiditis, number of preserved parathyroids, parathyroid autotransplantation, preoperative PTH, or preoperative calcium to be significantly predictive. Multivariate analysis confirmed no confounding factors in vitamin D analysis, but also did not show any independent risk factors. Fisher analysis comparing total versus completion thyroidectomy was marginally significant (P =. 064) for predicting hypocalcemia. Conclusion Transient hypocalcemia appears to occur less frequently in patients with VDD, however in our limited study we were unable to elucidate whether this was significant. Future study observing a larger population and excluding completion thyroidectomy patients may help to clarify whether VDD impacts risk for 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 | 0.000 | 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".