Digital Photography as a Novel Technique of Measuring Ocular Surface Dimensions
Bibliographic record
Abstract
PURPOSE: To introduce a novel technique for measuring ocular surface dimensions using digital photography and computerized image analysis in the context of ptosis repair surgery. METHODS: Digital photographs and patient questionnaires on dry eye symptoms were obtained from 31 patients before and after ptosis repair. Patients were examined preoperatively and at 1 and 6 weeks postoperatively. Adobe Photoshop 7.0 (Adobe Systems Incorporated, 345 Parkl Avenue, San Jose, CA 95110-2704, USA) was used to digitally measure palpebral fissure height, fissure width, and ocular surface area (OSA). Similar digital measurements were obtained on 30 control subjects as well. Digital calculations of OSA for control, preoperative, and postoperative groups were compared with other published techniques. RESULTS: Graphical comparison between our method of measuring OSA and the mathematical estimations proposed by previous studies suggests that our method is more precise in measuring OSA, and that it is also better able to identify individual variations of OSA from general population trends. CONCLUSION: Digital ocular photography combined with computerized image analysis is a fast, easy to use, and reliable method of measuring ocular surface dimensions. In addition to ptosis surgery, this method can be used in other ocular surface studies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".