Approaches of Turkish anesthesiologists to delirium observed in intensive care unit patients
Bibliographic record
Abstract
To determine attitudes and practices of the Turkish anesthesiologists and residents about delirium in the ICU. An anonymous questionnaire consisting of 22 questions [ 1 ] was mailed to 258 anesthesiologists and residents. One hundred and fifty-four questionnaires were returned (60% response). Of the respondents, 57% were male and 61% were residents. One-half of respondents work in hospitals with more than 800 beds; 65% of respondents had an ICU facility of 7–12 beds. Seventy-two percent of the respondents had seen delirium in the ICU and also 70.2% of these respondents observed delirium in <25% of patients who were on mechanical ventilation. Although delirium was accepted a significant or very serious problem by 92.5% of the respondents, underdiagnosis was acknowledged by 74%. Routine screening for delirium was performed by 41.6% of the anesthesiologists and 88.1% of them were repeating daily. Clinical assessment was used in 76.7% of the screenings. Delirium was treated with haloperidol and benzodiazepine by 61.5% and 24% of the respondents. Of the respondents, 93.4% were not able to attend a meeting related to delirium and 67.6% did not read even an article about delirium. Turkish anesthesiologists and residents consider delirium a relatively common and serious problem. However, they seldom perform screening tests and try to update their knowledge regarding delirium.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".