Effect of intranasal insulin administration on postoperative delirium prevention in elderly cardiac surgery patients: study protocol for a multicenter, double-blind, randomized, controlled trial
Bibliographic record
Abstract
BACKGROUND: Postoperative delirium (POD) is a complication after surgery which leads to worse outcomes. The frequency of this syndrome is increasing as more elderly patients undergo major surgery. The frequency is around 10-25% but reaches as high as 50% for cardiac surgery. Although intranasal insulin (INI) administration of up to 160 units in patients with cognitive dysfunction and delirium has been shown to improve memory function and brain metabolism without complications such as hypoglycemia, it remains unknown whether INI prevents POD after cardiac surgery METHODS: A multicenter, double-blind, randomized, controlled trial will be conducted at University of Tsukuba Hospital and Tsukuba Medical Center Hospital, Japan, from July 1, 2023, to December 31, 2025. A total of 110 elderly patients (65 years old or older) undergoing cardiac surgery requiring cardiopulmonary bypass will be enrolled and randomly allocated to intranasal insulin or intranasal saline groups. The primary outcome is the incidence of POD within 7 days after surgery. Secondary outcomes include days and times of delirium, screening tests of cognitive function, pain scores, duration of postoperative tracheal intubation, and length of ICU stay. DISCUSSION: The present objective is to assess whether 80 IU INI administration during surgery prevents POD after cardiac surgery. The results may provide strategic choices to prevent POD in patients with cardiac surgery requiring cardiopulmonary bypass. TRIAL REGISTRATION: The trial was registered with the Japan Registry for Clinical Trials with identifier jRCTs031230047 on April 21, 2023.
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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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.006 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.032 | 0.005 |
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".