The DISCERN (Discovering Innovative Strategies for prevention of delirium in Cardiac surgery patients through Evaluation of peri-operative Risk and Novel biomarkers) Study – Intermediate and long-term follow up of post-operative health-related quality of life and mental health status.
Bibliographic record
Abstract
Delirium is an acute form of “brain failure” characterized by fluctuation of attention and cognition. It is the most common neurological complication following cardiac surgery. The incidence of post-operative delirium in surgical patients varies between 10-60%, but can be as high as 73% in the elderly. Our study objectives were: 1) to examine the effect of post-operative delirium on mid- to long-term health-related quality of life (HRQoL) in patients following cardiac surgery, 2) to examine the effect of post-operative delirium on mental health following cardiac surgery and 3) to determine risk factors for each of these conditions. A prospective observational cohort study was carried out at a tertiary care centre in Winnipeg, Manitoba. Within the study cohort of 197 patients, the rate of post-operative delirium was 21.1% in elective cardiac surgery patients and 30.8% in urgent or emergent patient. Preoperative predictors of post-operative delirium were higher EuroSCORE II, previous cardiovascular procedure, older age, less than high school education and left ventricular ejection fraction of <35%. Intra-operative predictors of post-operative delirium were increased cardiopulmonary pump time, acute kidney injury, returning to the OR due to post-operative bleeding and new cerebrovascular accident. Delirious patients remained in the ICU almost 4 times longer than non-delirious patients and were hospitalized on average 5 days longer. Patients who suffered from post-operative delirium were 2 times more likely to score below average in the physical health aspect of the SF-12v2 and were nearly 3 times more likely to report having a problem with anxiety or depression.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".