Incidence and analysis of intraoperative complications in femtosecond laser-assisted cataract surgery: a large-scale cohort study to establish the learning curve
Bibliographic record
Abstract
AIMS: To assess the safety of femtosecond laser-assisted cataract surgery (FLACS) based on surgical parameters and intraoperative complications analysis and to determine the length of the learning curve for FLACS. METHODS: A prospective consecutive cohort study was conducted on Chinese patients who underwent either FLACS (3289 cases) or contemporaneous conventional phacoemulsification cataract surgery (2130 cases). The laser group was divided into four subgroups in chronological order. We recorded intraoperative complication incidences and compared with surgical parameters between groups. Subgroup analysis was conducted to explore the learning curve of FLACS. RESULTS: The laser group had a 4.93% incidence of incomplete capsulotomies and a 1.22% incidence of anterior capsule tears. Subgroup analysis showed significant differences in 8 aspects between the first 250 cases (50 cases per surgeon) and the last 2539 cases, but only 2 aspects differed between the second 250 cases (50 cases per surgeon) and the last 2539 cases. There were no significant differences between the third 250 cases (50 cases per surgeon) and the last 2539 cases. CONCLUSIONS: The intraoperative complications of FLACS were reported, and the learning curve is associated with a significant reduction in the incidence of intraoperative complications. The length of the basic learning curve of FLACS is 100 cases, and the length of the advanced learning curve was 150 cases. This study demonstrated that FLACS is characterised by a relatively straightforward and secure operative technique.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".