Assessment of nerve fiber layer thickness before and after laser in situ keratomileusis using scanning laser polarimetry with variable corneal compensation
Bibliographic record
Abstract
PURPOSE: To determine the effect of laser in situ keratomileusis (LASIK) on retinal nerve fiber layer (RNFL) thickness measurements obtained by scanning laser polarimetry with variable corneal compensation (SLP-VCC). SETTING: Gimbel Eye Centre, Calgary, Alberta. METHODS: Retinal nerve fiber layer thickness measurements were performed in both eyes of 25 consecutive healthy patients the day of LASIK surgery and 1 month after by trained examiners using the GDx-VCC nerve fiber analyzer. Thickness measurements and all other parameters provided by the software of the machine before and after LASIK were analyzed using the paired Student t test. RESULTS: Mean age of the patients was 39 years +/- 9.6 (SD) (range 24 to 57 years). The mean preoperative spherical equivalent was -4.15 +/- 1.76 diopters (D) (range -1.0 to -7.50 D) and the mean postoperative spherical equivalent, 0.12 +/- 0.39 D (range -0.75 to +1.00 D). Mean ablation depth was 62 +/- 23 mum. No statistically significant difference was found in SLP parameters after LASIK (P<.05). No clinically significant difference in RNFL thickness measurements was noted in any eye. CONCLUSION: These data suggest that SLP-VCC mean thickness measurements are not influenced by LASIK-induced alterations in corneal architecture. Measurements obtained with SLP-VCC before surgery may be used for future comparisons.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".