Process Improvement: Perioperative Eye Protection under General Anesthesia for Nonocular Surgery
Bibliographic record
Abstract
Purpose. This process improvement project aimed to promote systematic perioperative eye protection for patients who undergo general anesthesia for nonocular surgery.Background. Perioperative eye injuries can cause distress from severe pain, vision loss, and even blindness. Anesthesia practice pattern is associated with perioperative eye protection outcomes. Eye protection beyond taping the eyes with general anesthesia is institution and practitioner dependent. Clinical evidence indicated that consistent anesthesia department-wide eye protection practice changes improve outcomes. Presenting current practice-based evidence could promote local process improvement toward a sustained result. Methods. The Ottawa Model of Research Use Theory guided this education and survey-based project design. All eligible anesthesia providers (n=12) were invited to review a PowerPoint presentation of an innovative four-step eye protection Bundle and its associated theory and clinical evidence. Their current practice and future practice change were surveyed for each component of the Bundle. Two data points—the participation rate and the Bundle acceptance rate in the post-survey—have to reach a predetermined threshold to consider implementing any component of the Bundle. In addition, the post-survey must receive a higher approval rate than the corresponding pre-survey question. Results. The first data point is the participation rate which is 67% (8/12), over the threshold of 50%. The second data point is that Five Bundle components reached the threshold of 60% in the post-presentation survey with three of the five components showing significant differences from pre-presentation survey. The three components are Individualized eye protection- “Need-based product choice,” “implementing an anesthesia department-wide perioperative eye complication treatment protocol, and collecting data on eye-protection outcomes. Conclusions. This project identified three priority areas of practice change for local process improvement.
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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 | 0.031 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".