Transient Diagnostics and Therapeutic-Related Increase in Intraocular Pressure and Risk to the Glaucoma Patient
Bibliographic record
Abstract
Ticiana De Francesco,1– 3 Iqbal Ike K Ahmed3,4 1Hospital de Olhos Leiria de Andrade, Fortaleza, Brazil; 2Clinica de Olhos De Francesco, Fortaleza, Brazil; 3John A. Moran Eye Center, University of Utah, Salt Lake City, UT, USA; 4Department of Ophthalmology and Vision Sciences, University of Toronto, Toronto, ON, CanadaCorrespondence: Ticiana De Francesco, Email ticianadefrancesco@gmail.comAbstract: This review evaluates the impact of transient intraocular pressure (IOP) elevations during common ophthalmic surgical and diagnostic procedures on glaucoma patients. Elevated IOP is a key risk factor in glaucoma, and while transient IOP spikes are frequently encountered during surgeries like cataract extraction, laser in situ keratomileusis (LASIK), and femtosecond laser-assisted cataract surgery (FLACS), the clinical significance of these short-term elevations remains uncertain, particularly for eyes with compromised optic nerves. There is still a lack of data on which IOP level and duration of IOP insult the glaucoma damage occurs. Still, it is known that the combination of the degree of IOP elevation, duration of the insult and optic nerve susceptibility are important determinants of this event. While transient IOP elevations during these procedures are generally well tolerated, patients with advanced glaucoma and severely compromised optic nerves may be at greater risk for further damage. More research is needed to fully understand the long-term implications of acute IOP spikes, particularly in patients with advanced disease.Keywords: intraocular pressure, glaucomatous damage, transient intraocular pressure rise
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".