Incidence of Postoperative Hypocalcemia following Total Thyroidectomy versus Completion Thyroidectomy
Bibliographic record
Abstract
Objectives: Study hypocalcemia incidence and trends over time following completion thyroidectomy (CT) versus total thyroidectomy (TT). Methods: A retrospective study comparing hypocalcemia and hypoparathyroidism incidence rates in all patients who underwent CT and in a random control group of TT at the McGill University Thyroid Cancer Centre, during the period of 2007 through 2012. Data were collected for demographic, clinical, and pathological characteristics. Results: There were 68 CT patients and 146 TT patients. Transient hypocalcemia occurred in 1 out of 68 (2%) and 18 out of 146 (12%) inpatients in the CT and TT groups, respectively. The rate of hypocalcemia was significantly lower in the CT group when compared with the TT group (P =. 02). In both groups, there were no cases of permanent hypocalcemia. In the CT group there were 12 (18%) patients with parathyroid gland or parathyroid tissue found in the surgical specimen, compared with 47 (32%) of patients in the TT group. There were no correlations between the postoperative hypocalcemia rates and the type of the thyroid disease or the numbers of parathyroid glands removed and found within the surgical specimen (P >. 05). Conclusions: In this study, the risk of transient hypocalcemia in patients undergoing CT was significantly lower than the rate of hypocalcemia in patients undergoing TT.
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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 | 0.001 | 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".