Improvement of visual acuity and visual evoked patterned potentials done at different spatial frequencies after rehabilitation in 45 subjects affected by Cerebral Visual Impairment.
Bibliographic record
Abstract
Abstract Purpose To analyze improvement of visual acuity(VA) and visual evoked patterned potentials (pVEP) in children diagnosed by MR as cerebral visual impaired (CVI) after rehabilitation (refractive correction and occlusion terapy). Methods In 45 CVI infants(mean age 5.6)we analysed VA and pVEPs improvement after 1 year of follow up. Teller Acuity Cards and/or optotype were used for decimal visual acuity. P‐VEPs were recorded at Oz,O1,O2, referenced to Fz. At least two spatial frequencies (among 300’,120’,60’,30’,15’).Statistical analysis were made between VA and pVEP improvement. Results VEP and VA percentage of success was 100% and 89% respectively at the beginning; after 1 year VA percentage of success rised 100%. Mean VA before treatment was 2,29/10(0.2‐10); after treatment was 3,61/10(0.1‐10). VA improvement was statistically significant(z=0,00) VA improvement occurred in 68,89%, while 31,11% were unimproved. Improved VEP were 80% and unimproved 20%. We considered improved VEP when children were able to detect lower spatial frequencies or when, in the same spatial frequency, we found higher amplitudes and reduced latencies. Differences between VEP amplitude and latency were not statistically significant in all frequencies but in L60’ (T‐test L60':0.02). There was an association between VA improvement and therapy,(Pr = 0.02) There wasn’t association (Pr = 0.76) between VEP improvement and therapy. There wasn’t correlation between VA and VEP improvement. Conclusion We found an improvement both in VA and VEP.VEP improvement is independent of therapy,VA improvement is correlated with therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".