Diagnosis-related factors favoring the success of exotropia surgery
Bibliographic record
Abstract
Aim of the study: To identify factors favoring the success of surgery for exotropia.Material and methods: Fifty-nine patients with mostly bilateral exotropia were enrolled, 33 with basic constant exotropia (group 1) and 26 with intermittent exotropia (group 2).Patient age ranged from 10 years to 21 years, the mean corrected visual acuity was 0.8 ±0.3, and the mean refractive error was 0.61 ±2.3 D (range, -5.0 D to 7.0 D).The ocular motor and sensory system was assessed through eye examination and orthoptic methods.Success of surgery was defined as orthotropia of 10 prism diopters (PD) or less.Analysis of variance with the Newman-Keuls multiple comparisons test and the chi-square test were used for group comparisons.A multiple regression analysis was used to determine the relationships between the preoperative characteristics of the ocular motor and sensory systems and the outcome of surgery.Results: Our comparison of the surgery success group to the surgery failure group for preoperative values of accommodative convergence-accommodation (AC/A) ratio, near point of convergence (NPC) and state of stereopsis showed that near-normal preoperative levels of AC/A ratio (4.0 ±1.65 PD/D) and NPC (8.03 ±3.02 cm), the preoperative presence of distance stereopsis, near stereopsis of 200 seconds of arc, and the absence of medial rectus muscle hypofunction were characteristic for the former group.The multiple regression model developed confirmed that the NPC and the presence of distance stereopsis can be used as predictors of the success of exotropia surgery.Conclusion: NPC and the presence of distance stereopsis can be used as predictors of the success of exotropia surgery.
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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 | 0.012 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".