Refractive outcomes of immediately sequential bilateral cataract surgery in eyes with long and short axial lengths
Bibliographic record
Abstract
PURPOSE: To report the refractive outcomes of long (≥25.00 mm) and short (≤22.00 mm) axial length (AL) eyes undergoing immediately sequential bilateral cataract surgery (ISBCS). METHODS: In this retrospective cohort study, patients who underwent ISBCS were identified and eyes of patients with bilateral long and short ALs were included. Pre- and postoperative biometry, autorefraction, and ocular comorbidities or complications were recorded. The primary outcome was the mean refractive prediction error. RESULTS: Thirty-seven patients (74 eyes) with long ALs and 18 patients (36 eyes) with short ALs were included. The means ± standard deviations of the ALs were 26.40 ± 1.38 mm and 21.44 ± 0.46 mm in the long and short AL groups, respectively. In long AL eyes, the mean absolute error from the biometry-predicted refraction was - 0.16 ± 0.46 D, corresponding to 74% of eyes achieving a refraction within ±0.50 D of the predicted value. In short AL eyes, the mean absolute error was - 0.63 ± 0.73 D, corresponding to 44% of eyes achieving a refraction within ±0.50 D of the predicted value. Eight (44.4%) patients with short AL eyes had a myopic deviation greater than ±0.50 D from the predicted result in both eyes. CONCLUSIONS: Compared to patients with long AL eyes, ISBCS in patients with short ALs had a wider variance in refractive outcome and a lower rate of achieving a postoperative refraction within ±0.50 D of the predicted target.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".