Occult wound leak diagnosed by ultrasound biomicroscopy in patients with postoperative hypotony
Bibliographic record
Abstract
PURPOSE: To describe the ability of high-frequency ultrasound biomicroscopy (UBM) to diagnose occult wound leaks as a cause for hypotony after cataract surgery. METHODS: Six patients with persistent hypotony after cataract surgery were sent for UBM examination. Slitlamp examination and gonioscopy of the 6 eyes had not revealed a cause for the hypotony. RESULTS: Ultrasound biomicroscopy showed subtle wound separation with shallow conjunctival elevation at the site of the cataract wound in the 6 patients. Two eyes had surgical repair of the subconjunctival wound leak, and the other 4 were treated medically. In the 2 eyes with surgically repaired wounds, the hypotony cleared after wound closure. Of the 4 medically treated eyes, hypotony resolved in 2 and 1 had a recurrence of hypotony. The other 2 eyes had fluctuating intraocular pressure for an extended period. CONCLUSIONS: Hypotony after cataract surgery occurred in 6 eyes due to subtle wound leaks difficult to detect by clinical observation. Ultrasound biomicroscopy can be a helpful aid to clinical examination in detecting these leaks.
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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 | 0.000 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".