Imaging Through Opaque Corneas Using Anterior Segment Optical Coherence Tomography
Bibliographic record
Abstract
<h4>BACKGROUND AND OBJECTIVE</h4> <p>To investigate the capability of the prototype AC Cornea OCT (Ophthalmic Technologies Inc., Toronto, Ontario, Canada) for imaging eyes with opaque corneas.</p> <h4>PATIENTS AND METHODS</h4> <p>More than 50 eyes of patients were included in the study.</p> <h4>RESULTS</h4> <p>The density of opacification influenced the ability of OCT to reveal anterior segment details. Imaging was limited by shadows cast by abnormal pigmentation or vascularity within the cornea. However, the system&rsquo;s unique coronal perspective capability was helpful in revealing occult spatial relationships.</p> <h4>CONCLUSIONS</h4> <p>The AC Cornea OCT is valuable for examining eyes with opaque corneas and provides cross-sectional and coronal views helpful in formulating specific management strategies.</p> <p>[<cite>Ophthalmic Surg Lasers Imaging</cite> 2007;38:314-318.]</p> <h4>AUTHORS</h4> <p>From The New York Eye &amp; Ear Infirmary (JPSG, PMTG, DFB, AP, RBR), New York; and New York Medical College (JPSG, PMTG, DFB, RBR), Valhalla, New York.</p> <p>Accepted for publication December 4, 2006.</p> <p>Presented at the annual meeting of the International Society for Imaging in the Eye, Fort Lauderdale, Florida, April 28-29, 2006.</p> <p>Address correspondence to Julian P. S. Garcia Jr., MD, The New York Eye &amp; Ear Infirmary Retina Center, 310 East 14th St., New York, NY 10003.</p>
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 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".