The effect of hypercapnia on the sensitivity to flicker defined stimuli
Bibliographic record
Abstract
BACKGROUND/AIMS: To investigate the effect of increased CO2 levels on flicker defined stimuli. METHODS: The sensitivity of two flicker defined tasks was measured in nine healthy, trained observers using the Flicker Defined Form (FDF) stimulus of the Heidelberg Edge Perimeter (HEP; Heidelberg Engineering) and Frequency Doubling Technology (FDT) stimulus of the Matrix perimeter (Carl Zeiss Meditec) during normoxia and 15% hypercapnia (end-tidal CO2 increased by 15% relative to baseline). HEP-FDF and Matrix-FDT sensitivities were analysed for the global field, superior and inferior hemifields and at specific matched eccentricities, using repeated measures analysis of variance. The main effect of hypercapnia on flicker sensitivity was analysed using regression models. RESULTS: Higher flicker sensitivity outcomes with increasing CO2 values were found for HEP-FDF and Matrix-FDT with a statistically significant main effect for HEP-FDF global, superior and inferior hemifields (p<0.01 for all) as well as 6°, 18°, 12° and 24° eccentricities (p=0.03, 0.04, 0.01, 0.05, respectively). When comparing mean sensitivity values between normocapnia and hypercapnia conditions, no statistically significantly different results were found for HEP-FDF and Matrix-FDT (p>0.05). CONCLUSIONS: As CO2 levels were increased in healthy young individuals, there was an associated increase in visual sensitivity that was only significant for HEP-FDF stimuli, highlighting the different mechanisms involved in processing each of HEP-FDF and Matrix-FDT stimuli. Mean visual sensitivity outcomes were found to be similar for normocapnia and hypercapnia suggesting that a capability to compensate for a mild and stable increase in systemic CO2 levels may exist.
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 | 0.002 |
| 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 | 0.000 |
| 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".