Can the Iowa Satisfaction with Anesthesia Scale Be Used to Measure Patient Satisfaction with Cataract Care Under Topical Local Anesthesia and Monitored Sedation at a Community Hospital?
Bibliographic record
Abstract
In Brief Patient satisfaction ratings provide a means to evaluate and monitor quality of health care. We tested the ability of the Iowa Satisfaction with Anesthesia Scale (ISAS) to measure satisfaction with cataract care under topical local anesthesia and monitored sedation given by an anesthesiologist at a community hospital. Three hundred six patients were administered the ISAS along with alternate ratings of quality of care and patient satisfaction. There were no incomplete questionnaires. The ISAS demonstrated reasonable reliability (Cronbach’s α = 0.68; test-retest = 0.48–0.67). The ISAS had excellent construct validity; ISAS scores were lower in patients who gave lower ratings of quality (4.98 versus 5.64), who had lower satisfaction visual analog scale scores (5.12 versus 5.65), who wanted changes in their care (4.76 versus 5.67), who had suggestions to improve care (5.08 versus 5.63), or who preferred more sedation (4.85 versus 5.66) (P < 0.0001). Our results indicate that the ISAS questionnaire is a feasible, reliable, and valid tool to measure patient satisfaction in patients undergoing cataract surgery under topical anesthesia and monitored sedation. IMPLICATIONS: When tested on 306 cataract patients undergoing monitored anesthesia care, the Iowa Satisfaction with Anesthesia Scale demonstrated sufficient reliability and validity to allow it to be used as a means to track and compare the satisfaction of patients undergoing cataract surgery under monitored sedation.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".