Preoperative Anxiety in Neurosurgical Patients
Bibliographic record
Abstract
Anxiety is common in surgical patients, with an incidence of 60% to 92%. There is little information on the incidence and severity of preoperative anxiety in patients scheduled for neurosurgery. The aim of this study was to measure the level of preoperative anxiety in neurosurgical patients and to assess any influencing factors. After the Institutional Review Board approval and informed written consent, 100 patients booked for neurosurgery were interviewed preoperatively. Each patient was asked to grade their preoperative anxiety level on a verbal analog scale, Amsterdam Preoperative Anxiety and Information Scale, and a set of specific anxiety-related questions. The anxiety scores and the responses to the questions were compared between the sex, age, weight, diagnosis, and history of previous surgery. The mean age (+/-SD) was 50+/-13 years. The preoperative diagnosis was tumor (n=64), aneurysm (n=14), and other (n=22). Overall verbal analog scale was 5.2+/-2.7; the score was higher for female (5.8+/-2.8) than male patients (4.6+/-2.5) (P<0.05). Amsterdam Preoperative Anxiety and Information Scale anxiety and knowledge scores were greater for surgery than for anesthesia. Questionnaire results showed that the most common anxieties were waiting for surgery, physical/mental harm, and results of the operation. In conclusion, our study showed that neurosurgical patients have high levels of anxiety, with a higher incidence in females. There was a moderately high need for information, particularly in patients with a high level of preoperative anxiety.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".