Sources of Medical Error in Refractive Surgery
Bibliographic record
Abstract
PURPOSE: To evaluate the causes of laser programming errors in refractive surgery and outcomes in these cases. METHODS: In this multicenter, retrospective chart review, 22 eyes of 18 patients who had incorrect data entered into the refractive laser computer system at the time of treatment were evaluated. Cases were analyzed to uncover the etiology of these errors, patient follow-up treatments, and final outcomes. The results were used to identify potential methods to avoid similar errors in the future. RESULTS: Every patient experienced compromised uncorrected visual acuity requiring additional intervention, and 7 of 22 eyes (32%) lost corrected distance visual acuity (CDVA) of at least one line. Sixteen patients were suitable candidates for additional surgical correction to address these residual visual symptoms and six were not. Thirteen of 22 eyes (59%) received surgical follow-up treatment; nine eyes were treated with contact lenses. After follow-up treatment, six patients (27%) still had a loss of one line or more of CDVA. Three significant sources of error were identified: errors of cylinder conversion, data entry, and patient identification error. CONCLUSION: Twenty-seven percent of eyes with laser programming errors ultimately lost one or more lines of CDVA. Patients who underwent surgical revision had better outcomes than those who did not. Many of the mistakes identified were likely avoidable had preventive measures been taken, such as strict adherence to patient verification protocol or rigorous rechecking of treatment parameters.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".