Documenting the subjective patient experience of first vs second eye during immediately sequential bilateral cataract surgery
Bibliographic record
Abstract
PURPOSE: To investigate if patients had a subjectively inferior experience during second-eye surgery vs first-eye surgery during immediate sequential bilateral cataract surgery (ISBCS) as both operations were performed within the same surgical visit. SETTING: Multisurgeon ophthalmic surgical center. DESIGN: Survey-based prospective design. METHODS: Patients of the center undergoing routine cataract surgery who enrolled in the study completed a questionnaire immediately after surgery to describe their surgical experience for each eye. Survey questions measured patient pain, discomfort or pressure, comfort, relaxation, estimated length of surgery, and predicted visual outcome. RESULTS: Patient-reported level of pain during the second-eye surgery was significantly greater than for the first eye ( P < .001). Furthermore, the order of surgery was found to be a strong predictor of patient-reported pain ( P < .001), more so than surgical length ( P < .008), additional anesthesia ( P < .35), patient age ( P < .44), or patient sex ( P < .88). Overall, surgery during the first eye was reported as more comfortable ( P < .001) and shorter in duration ( P < .001), while discomfort and pressure were reported as worse for the second eye ( P < .001). CONCLUSIONS: Understanding differential experiences between eyes can help surgeons when counselling patients regarding expectations for ISBCS and related visual outcomes.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".