Twenty four-hour intraocular pressure fluctuation in treated glaucoma patients: a pilot study
Bibliographic record
Abstract
OBJECTIVE: To conduct a pilot study to evaluate and compare the 24-hour habitual intraocular pressure (IOP) and ocular perfusion pressure (OPP) fluctuation in glaucoma patients treated with medical therapy, selective laser trabeculoplasty (SLT) or trabeculectomy. DESIGN: Pilot study. PARTICIPANTS: Criteria for inclusion were patients aged 18 years or older with well-controlled IOP with either maximum tolerated medical therapy, previous SLT, or previous trabeculectomy. METHODS: Recruited patients were admitted to the sleep lab for 24-hour serial habitual IOP and blood pressure measurements. IOP and OPP fluctuation among the 3 treatment groups were compared. RESULTS: Thirty three (33) eyes from 33 patients were recruited in this study, including 11 patients in the medical therapy group, 11 patients in the SLT group, and 11 patients in the trabeculectomy group. The medical therapy group was found to have significantly higher 24-hour IOP fluctuation (8.3 ± 1.6 mmHg) than the SLT (3.5 ± 1.9 mmHg) and trabeculectomy (4.3 ± 1.3 mmHg) groups (P < 0.001). Mean 24-hour OPP fluctuation was also significantly higher in the medical therapy group (18.5 ± 4.0 mmHg) than the SLT (11.9 ± 7.3 mmHg) and trabeculectomy (14.1 ± 3.9 mmHg) groups (P < 0.05). No difference in IOP or OPP fluctuation was found between SLT and trabeculectomy groups (P > 0.05). CONCLUSIONS: Both SLT and trabeculectomy may be more effective in reducing 24-hour IOP and OPP fluctuation than medical therapy alone. IOP and OPP fluctuation was comparable between SLT and trabeculectomy cohorts. Future studies are warranted to investigate this further.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".