OPTICAL COHERENCE TOMOGRAPHY–BASED CORRELATION BETWEEN CHOROIDAL THICKNESS AND DRUSEN LOAD IN DRY AGE-RELATED MACULAR DEGENERATION
Bibliographic record
Abstract
In Brief Purpose: Spectral domain optical coherence tomography can be used to measure both choroidal thickness and drusen load. The authors conducted an exploratory study using spectral domain optical coherence tomography to determine if a correlation between choroidal thickness and drusen load exists in patients with dry age-related macular degeneration. Methods: Forty-four patients with dry age-related macular degeneration were recruited. The drusen area and volume were determined using the automated software algorithm of the spectral domain optical coherence tomography device, and choroidal thickness was measured using enhanced depth imaging. Correlations were determined using multivariable and univariable analyses. Results: The authors found an inverse correlation between choroidal thickness and drusen load (r = −0.35, P = 0.04). Drusen load was also correlated with visual acuity (r = 0.32, P = 0.04). A correlation between choroidal thickness and visual acuity was suggested (r = −0.22, P = 0.21). Conclusion: Spectral domain optical coherence tomography can be used to assess the correlation between drusen load and choroidal thickness, both of which show a relationship with visual acuity. The measurement of these outcomes may serve as important outcome parameters in routine clinical care and in clinical trials for patients with dry age-related macular degeneration. The study looks at the relationship between drusen load and choroidal thickness in patients with dry age-related macular degeneration using spectral domain optical coherence tomography. The authors found an inverse relationship between the two variables. Choroidal thickness may be a useful parameter in the study and treatment of dry age-related macular degeneration.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".