Assessment of total retinal blood flow using Doppler Fourier Domain Optical Coherence Tomography during systemic hypercapnia and hypocapnia
Bibliographic record
Abstract
The purpose of this study was to investigate changes in total retinal blood flow (RBF) using Doppler Fourier Domain Optical Coherence Tomography (Doppler FD-OCT) in response to the manipulation of systemic partial pressure of CO2 (PETCO2). Double circular Doppler blood flow scans were captured in nine healthy individuals (mean age ± standard deviation: 27.1 ± 4.1, six males) using the RTVue(™) FD-OCT (Optovue). PETCO2 was manipulated using a custom-designed computer-controlled gas blender (RespirAct(™)) connected to a sequential gas delivery rebreathing circuit. Doppler FD-OCT measurements were captured at baseline, during stages of hypercapnia (+5/+10/+15 mmHg PETCO2), return to baseline and during stages of hypocapnia (-5/-10/-15 mmHg PETCO2). Repeated measures analysis of variance (reANOVA) and Tukey's post hoc analysis were used to compare Doppler FD-OCT measurements between the various PETCO2 levels relative to baseline. The effect of PETCO2 on TRBF was also investigated using linear regression models. The average RBF significantly increased by 15% (P < 0.0001) with an increase in PETCO2 and decreased significantly by 10% with a decrease in PETCO2 (P = 0.001). Venous velocity significantly increased by 3.11% from baseline to extreme hypercapnia (P < 0.001) and reduced significantly by 2.01% at extreme hypocapnia (P = 0.012). No significant changes were found in the average venous area measurements under hypercapnia (P = 0.36) or hypocapnia (P = 0.40). Overall, increased and decreased PETCO2 values had a significant effect on RBF outcomes (P < 0.002). In healthy individuals, altered end-tidal CO2 levels significantly changed RBF as measured by Doppler FD-OCT.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".