Relationship between Diastolic Perfusion Pressure and Progressive Optic Neuropathy as Determined by Heidelberg Retinal Tomography Topographic Change Analysis
Bibliographic record
Abstract
PURPOSE: To determine through retrospective file analysis which clinical factors best predict glaucomatous optic neuropathy as evaluated by Heidelberg retinal tomography (HRT II) imaging. METHODS: One HUNDRED twenty-two records from patients referred for HRT imaging at the University of Waterloo Ocular Heath Clinic met inclusion criteria for this study and were reviewed. Topographic change analysis (TCA) data generated by HRT were examined in addition to the following clinical information: diastolic blood pressure, right arm sitting, intraocular pressure, and central corneal thickness. All HRT scans included were required to have 20 μm or better standard deviation (SD) on acquisition and deemed "very good" quality or "excellent" by HRT software. Based on previously defined published HRT TCA change criteria, each patient was allocated to one of the following groups: stable, borderline, or progressive. RESULTS: Diastolic perfusion pressure (DPP) was found to be significantly lower in the borderline and progressive groups compared with the stable group (P < 0.001). DPP was also lower significantly lower in the progressive group compared with the borderline group (P < 0.001). CONCLUSIONS: Low DPP appears to be a reasonable predictor of progressive optic neuropathy as determined using scans of <20 μm SD on the HRT TCA platform. DPP of 56 mm Hg or lower appears to be a clinically useful threshold to identify patients at increased risk of progressive optic neuropathy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".