How does Bangerter filter affect the normal visual perception?
Bibliographic record
Abstract
Abstract Purpose The Bangerter filter (BF) has become an alternate form of amblyopia patching treatment due to its high social‐psychological compliance and effective therapeutic outcomes. This study aims to investigate the effects of different BF densities on the normal visual system in order to quantify filter densities based on vision perception variables. Methods Thirty‐two binocularly normal subjects participated in this study. Visual acuity (VA), vernier acuity (VNA) and contrast sensitivity (CS) under spatial frequencies (SF) of 0.5, 1, 2, 5, 10 cyl/deg were measured while the dominant eye was patched with randomly selected BF of densities: 0.8, 0.6, 0.4, 0.2. Results Mean VA and VNA values under the four BF densities were a) 0.29, 0.30, 0.35, 0.54 logMAR and b) 3.24, 4.81, 5.29, 8.19 min of arc respectively. There was a significant difference (p<0.05) in VA change between BF 0.2 and rest of the density groups. BF 0.8 and 0.2 groups showed a significant VNA change compared to BF 0.6 and 0.4 (p<0.05). There were no significant effects noted in CS at SF of 0.5, 1 and 2 cyl/deg across low to high BF groups (p>0.05). BF 0.2 was the only density that had a significant influence on middle and high SF (5 and 10 cyl/deg) (p<0.05). Conclusion BF does not alter vision perception abilities in a predictable manner suggested by the manufacturer. Only the high BF density has shown a significant change. Careful attention must be paid on the inconsistencies in low and middle BF densities for the patching treatment.
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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) | 0.002 | 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".