Straightening the Eyes Doesn't Rebalance the Brain
Bibliographic record
Abstract
Surgery to align the two eyes is commonly used in treating strabismus. However, the role of strabismic surgery on patients’ binocular visual processing is not yet fully understood. In this study, we asked two questions: 1. Does realigning the eyes by strabismic surgery produce an immediate benefit to patients’ sensory eye balance? 2. If not, is there a subsequent period of “alignment adaptation” akin to refractive adaptation where sensory benefits to binocular function accrue? Seventeen patients with strabismus (mean age: 17.06 ± 5.16 years old) participated in our experiment. All participants had normal or corrected to normal visual acuity (LogMAR < 0.10) in the two eyes. We quantitatively measured their sensory eye balance before and after surgery using a binocular phase combination paradigm. For the seven patients whose sensory eye balance was measured before surgery, we found no significant change (t(6) = -0.88; p = 0.41) in the sensory eye balance measured 0.5-1 months after the surgery, indicating that the surgical re-alignment didn’t by itself produce any immediate benefit for sensory eye balance. To answer the second question, we measured 16 patients’ sensory eye balance at around 5-12 months after their eyes had been surgically re-aligned and compared this with our measurements 0.5-1 months after surgery. We found no significant change (t(15) = -0.89; p = 0.39) in sensory eye balance 5-12 months after the surgery. These results suggest that strabismic surgery while being necessary is not itself sufficient for re-establishing balanced sensory eye dominance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".