Evaluating the safety of air travel for patients with scleral buckles and small volumes of intraocular gas
Bibliographic record
Abstract
AIM: To evaluate the effect of scleral buckling on intraocular pressure (IOP) change during atmospheric decompression for eyes with small volumes of intravitreal gas. METHODS: 12 eyes of 12 patients, including 6 with and 6 without scleral buckles, were evaluated in a hypobaric chamber simulating air travel approximately 1 month post pars plana vitrectomy with 15% C3F8 gas fluid exchange. The chamber was decompressed with an ascent rate of 300 feet/min to a peak altitude of 8000 feet. After 15 min of cruising, descent was undertaken at 300 feet/min. IOP was measured at baseline and then every 5 min using slit-lamp mounted Goldmann applanation tonometry. The data were entered onto a spreadsheet and comparative statistics were done. RESULTS: During ascent, IOP steadily rose from an average of 13±3 mm Hg to a peak of 26±9 mm Hg at 8000 feet. Patients with scleral buckles had significantly lower peak IOPs compared with those without buckles (20±5 mm Hg vs 32±8 mm Hg, p=0.013, t test) representing lower absolute increases in IOP (7±1 mm Hg vs 19±7 mm Hg, p=0.001, t test) and lower percentage increases in IOP from baseline (62±25% vs 140±40%, respectively). CONCLUSIONS: Eyes with small volumes of intravitreal gas demonstrate significant IOP changes during atmospheric decompression in simulated flight. The presence of a scleral buckle significantly limits the magnitude of IOP change, suggesting that such patients can likely tolerate typical air travel without undue risk of dangerous IOP elevation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".