Comparison of Operating Room Utilization Time Between Immediate and Delayed Sequential Bilateral Cataract Surgery at a Tertiary Hospital in Thailand
Bibliographic record
Abstract
Purpose: To compare operating room (OR) utilization time between immediate sequential bilateral cataract surgery (ISBCS) and delayed sequential bilateral cataract surgery (DSBCS) at a tertiary care hospital in Thailand. Patients and Methods: This prospective observational study was conducted at King Chulalongkorn Memorial Hospital, Bangkok, Thailand. From April to September 2024, a total of 99 patients were recruited (45 ISBCS, 54 DSBCS). Nine DSBCS patients were excluded due to surgery intervals exceeding 12 weeks, resulting in 90 patients (45 per group) for analysis. Inverse probability of treatment weighting (IPTW) was applied to balance baseline covariates. Weighted linear regression models were used to estimate treatment effects on total OR time and its components, including preparation, draping, surgical, exit, and turnover times. Results: Baseline characteristics showed ISBCS group had more surgeries performed by younger surgeons (p = 0.014) and more frequent use of toric (p = 0.034) and preloaded IOLs (p = 0.004). Average OR utilization time was 55.8 minutes (SD = 12.0) for ISBCS and 60.0 minutes (SD = 12.9) for DSBCS. After IPTW adjustment, ISBCS reduced total OR time by 5.81 minutes (95% CI 0.87 to 10.76) compared to DSBCS. This reduction remained consistent across multivariate models (5.86-5.99 minutes). Time savings were primarily attributed to shorter surgical time (p = 0.006), exit time (p < 0.001), and turnover times (p = 0.002). Surgeon experience had the most significant impact on OR time. Conclusion: ISBCS reduced total OR utilization time by approximately 6 minutes (10%) per patient compared to DSBCS, enhancing operational efficiency and potentially increasing surgical capacity.
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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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".