Risk factors for postoperative complications in total thyroidectomy
Bibliographic record
Abstract
Thyroid cancer incidence is increasing, and with it, an increase in total thyroidectomy. There are limited studies comparing outcomes in total thyroidectomy performed in the inpatient versus outpatient setting.The objective of this study was to perform a comparative analysis of risk factors and outcomes of postoperative morbidity and mortality in total thyroidectomy performed as an inpatient versus outpatient surgery.Retrospective cohort study of data from the 2005 to 2014 multi-institutional, risk-adjusted American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database. A multivariate regression model with corresponding odds ratios and 95% confidence intervals was used to determine 30-day morbidity and mortality after total thyroidectomies, and also risk factors of postoperative outcomes.From 2005 to 2014, 40,025 total thyroidectomies were performed (48.5% inpatient, 51.5% outpatient). The 30-day complication rate for all total thyroidectomies was 7.74%. Multivariate logistic regression analysis was performed to control for potential confounding variables. Preoperative factors that affected complications rates for inpatient thyroidectomies included: age ≥70, non-Caucasian race, dependent functional status, history of congestive heart failure, smoking history, bleeding disorder, wound infection, and preoperative sepsis (P < 0.05). In addition, preoperative factors affecting complications in thyroidectomy performed as an outpatient surgery included malignant thyroid pathology (P 0.05).We identified a subset of preoperative conditions that affect risk of complications after total thyroidectomy. Recommendations for patient selection for outpatient total thyroidectomies should be modified to account for pre-existing conditions that increase the risk of postoperative morbidity.
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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 | 0.001 |
| 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".