Mechanisms underlying the etiology and treatment of Convergence Insufficiency
Bibliographic record
Abstract
Introduction: Developmental anomalies that arise within the cross-linkages between ocular vergence and accommodation such as convergence insufficiency (CI) are associated with complaints of blur and diplopia. We hypothesize that reduced vergence adaptation (VAdapt) found in CI[1,2,3] leads to an excess of convergence driven accommodation (CA). Further, the observed improvement in (VAdapt) following "vision training" (VT)[4,5] leads to an improved control of CA output. Method: Nine participants (X=17.4±2.3 yrs ) recruited from an eye clinic, met CI criteria (reduced prism acceptance based on established norms and/or Sheard's criterion). The asymptotic reduction in phoria (while viewing through 12? base out at 40cm taken in 3 min intervals over 15 minutes) defined VAdapt. Concurrent measures of CA were obtained using the MCS PowerRefractor, while the subject viewed a 0.2-cpd DOG target. VT was prescribed for a 12 week period with weekly clinical checks . CA and VAdapt measures were repeated at 5 and 12 weeks. Six CI participants completed. Six controls were recruited. Results: CI's showed a significantly less VAdapt and higher CA output (P=0.014 and 0.017 respectively) compared with controls. After 12 weeks of VT (but not 5 weeks) this difference disappeared (P>0.05). Clinical findings normalized after 5 weeks but symptoms were not ameliorated until 12 weeks. Conclusion: Both hypotheses were retained. Reduced VAdapt in CI leads to excessive levels of CA. When VAdapt is enhanced with VT, excessive CA is normalized. Symptom relief was linked more with VAdapt and CA correction than with normalized clinical findings. Meeting abstract presented at OSA Fall Vision 2012
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".