Assessment of Closing Pressure in Silicone Ahmed FP7 Glaucoma Valves
Bibliographic record
Abstract
PURPOSE: To measure closing pressure in a series of new Model FP7 silicone Ahmed Glaucoma Valves (AGVs), using an in vitro gravity-driven system, in which closing pressure was measured directly from a dual manometer/pressure reservoir. METHOD: A straight length of tubing served as an open manometer, which connected via a 3-way stopcock to the inlet of the AGV submerged under 1.5 cm of balanced saline solution (BSS). Six AGV were assessed in 5 sequential trials and the mean inlet pressure at 90 minutes was recorded as the closing pressure. After testing, a single valve was primed a second time using greater force and retested. Control trials were performed with a submerged 26-G cannula opening directly into the BSS bath. RESULTS: At 90 minutes, the 6 valves equilibrated at a mean inlet pressure of 7.1 mm Hg, with a range from 1.4 to 13.5 mm Hg. Closing pressures at or less than 5 mm Hg were measured in half (3/6) of the valves tested. The average flow of BSS across the valves in the final 30 minutes was less than 1.5x10(-2) microL/min. In the single valve reperfused under greater pressure, the closing pressure increased. CONCLUSIONS: Model FP7 AGVs, in vitro, exhibit significant variability in closing pressure, with half closing at intraocular pressures considered potentially problematic in clinical situations. The results of the high-pressure perfusion experiment suggest that more research into the priming process is required so a precise description can be developed for surgeons.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".