Preoperative Factors Affecting Stereopsis after Surgical Alignment of Acquired Partially Accommodative Esotropia
Bibliographic record
Abstract
BACKGROUND/AIM: Despite successful ocular realignment, many strabismus patients never develop high levels of stereopsis. The purpose of this study was to determine preoperative factors that affect postoperative stereopsis in patients with acquired partially accommodative esotropia (APAET). METHODS: This was a retrospective chart review of patients who underwent successful surgery for APAET. We compared preoperative factors between patients achieving postoperative stereopsis of 100 seconds of arc or better versus those with worse than 100 seconds of arc. RESULTS: Fifty-seven patients met our inclusion criteria. Twenty-four (42%) had a final stereopsis of 100 seconds of arc or better. The mean age of onset of esodeviation for patients attaining stereopsis of 100 seconds of arc or better was 31.8 ± 12.9 months, versus 23.9 ± 10.0 months (p = 0.012) for patients with worse than 100 seconds of arc. Duration of constant misalignment was not significantly different between the two groups (30.1 ± 18.5 for patients attaining 100 seconds of arc versus 27.3 ± 18.6 months; p = 0.57). A multivariate regression analysis found older age of onset to be the only predictive factor for achieving better postoperative stereopsis (odds ratio 1.065, 95% CI: 1.014-1.118). CONCLUSION: Age of onset appears to be the most important factor affecting postoperative stereopsis in patients with APAET. Patients with an age of onset after 36 months tended to have better outcomes regardless of the duration of misalignment. Duration of misalignment and age at surgery did not have a significant impact on postoperative stereopsis in our patient population.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".