Neurocognitive disorders after thyroid surgery: a randomized controlled trial
Bibliographic record
Abstract
Objective. To improve the outcomes after thyroid surgery in female patients by identifying the optimal anesthetic options with minimal impact on cognitive potential. Material and methods. A single-center randomized longitudinal parallel-group controlled study included 120 female patients (aged 32—72 years, ASA II—III) who underwent thyroid surgery. Randomization was performed using the envelope method. In the SEVO group (n=40), anesthesia induction and maintenance were provided with sevoflurane; in the PROP group (n=40) — with propofol. In the COA group (n=40), induction was performed with propofol and anesthesia maintenance — with sevoflurane. The control group (for dichotomous Z-score of cognitive status) included 40 women (ASA I—II) without thyroid diseases. Monitoring: Harvard standard; anesthetic gas mixture analysis; consciousness depression degree (BIS); heart rate variability. We assessed cognitive status using the Montreal Cognitive Assessment Scale (MoCA test) before surgery, in 7 and 30 days after surgery. Results. In thyroid diseases, preoperative cognitive impairment was observed in 11.7% of patients. In patients with thyroid cancer, the risk of preoperative cognitive dysfunction was 11 times higher (p< 0.0001). The incidence of delayed neurocognitive recovery did not differ between groups (p=0.621). Delayed neurocognitive recovery increased the risk of prolonged hospital-stay by 3 times (OR 3.0248; 95% CI 1.3433—6.8114). The incidence of verified postoperative neurocognitive impairment was 50%, 45% and 15%, respectively (p=0.0002). COA reduced the risk of cognitive dysfunction by 5.1 times (OR 0.1950; 95% CI 0.0783—0.5158; p=0.001). Conclusion. In surgical treatment of thyroid diseases, the optimal option for anesthetic management regarding neuropsychological status is general anesthesia (induction with propofol, maintenance of anesthesia with sevoflurane).
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".