What Determines Patient Satisfaction with Cataract Care Under Topical Local Anesthesia and Monitored Sedation in a Community Hospital Setting?
Bibliographic record
Abstract
In Brief The Iowa Satisfaction with Anesthesia Scale (ISAS) is a reliable and valid tool to measure patient satisfaction with monitored anesthesia care. We used the ISAS to discover determinants of patient satisfaction with cataract care under topical local anesthesia and monitored sedation in a small community hospital. The ISAS (scored 1 to 6) was administered to 306 patients immediately after cataract surgery. All patients received topical local anesthesia and IV sedation administered by an anesthesiologist. Patient satisfaction was high: mean ISAS was 5.6 (sd 0.46; range: 3.3–6.0). The incidence of intraoperative and postoperative pain was 13% and 37%; other adverse events were infrequent (<5%). In multivariable logistic regression, significant predictors of satisfaction were postoperative pain (odds ratio [OR]: 4.84; 99% confidence interval [CI]: 2.21, 10.60), surgeon (OR: 0.21; 99% CI: 0.05, 0.91), and preoperative anxiety (OR: 1.17; 99% CI: 1.03, 1.34). ISAS mean scores (OR = 0.28; 99% CI: 0.13, 0.59) and preoperative anxiety (OR = 1.12; 99% CI: 0.99, 1.28) emerged as significant predictors of low rating of quality of experience. Our results indicate that the ISAS can be used to track patient satisfaction with monitored cataract care. Pain during and after cataract surgery is common and is a major reason for lower patient satisfaction with their cataract care. IMPLICATIONS: We used the Iowa Satisfaction with Anesthesia Scale, a validated patient satisfaction questionnaire, to discover determinants of satisfaction with cataract care under topical local anesthesia and monitored sedation in 306 patients at a community hospital. Overall patient satisfaction was high. Mild pain is common and is significantly associated with lower satisfaction scores.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".