The Influence of Preoperative Waiting Time on Anxiety and Pain Levels in Outpatient Surgery for Breast Diseases
Bibliographic record
Abstract
OBJECTIVE: This study aims to examine the effects of different preoperative waiting times on anxiety and pain levels in patients undergoing outpatient surgery for breast diseases, providing insights for clinical interventions during the perioperative phase. METHODS: Patients who underwent outpatient surgery at a hospital breast center in Ningbo between January 2021 and December 2021 were selected. Their anxiety levels at the time when they entered the preoperative preparation room and when they ended the postoperative waiting period for the rapid frozen section procedure were assessed using the State Anxiety Inventory (S-AI) questionnaire, and their pain levels at the end of the postoperative waiting period were assessed using the short-form McGill Pain Questionnaire. The patients enrolled were divided into 3 groups according to the preoperative waiting time: <2 hours (T1 group), 2 to 4 hours (T2 group), and >4 hours (T3 group); there were 150 patients in each group, and the anxiety and pain levels were compared between the groups. RESULTS: At the time of entering the preoperative preparation room, patients' S-AI score T1 = T2 ( P > 0.05), both T1 and T2 < T3 ( P < 0.05); however, at the time of the postoperative waiting period, patients' S-AI score was T1 < T2 < T3 ( P < 0.05), and the postoperative waiting period patients' short-form McGill Pain Questionnaire scores were T1 = T2 < T3 ( P < 0.05). CONCLUSIONS: The perioperative anxiety and pain levels of patients undergoing outpatient breast surgery increased with the prolongation of preoperative waiting time; 4 hours was the critical time point for change, after which the anxiety and pain levels of patients increased significantly.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".