Vascular dysfunction following breath-hold diving
Bibliographic record
Abstract
The pathogenesis of predominantly neurological decompression sickness (DCS) is multifactorial. In SCUBA diving, besides gas bubbles, DCS has been linked to microparticle release, impaired endothelial function, and platelet activation. This study focused on vascular damage and its potential role in the genesis of DCS in breath-hold diving. Eleven breath-hold divers participated in a field study comprising eight deep breath-hold dives with short surface periods and repetitive breath-hold dives lasting for 6 h. Endothelium-dependent vasodilation of the brachial artery, via flow-mediated dilation (FMD), and the number of microparticles (MPs) were assessed before and after each protocol. All measures were analyzed by two-way within-subject ANOVA (2 × 2 ANOVA; factors: time and protocol). Absolute FMD was reduced following both diving protocols (p < 0.001), with no interaction (p = 0.288) or main effect of protocol (p = 0.151). There was a significant difference in the total number of circulating MPs between protocols (p = 0.007), where both increased post-dive (p = 0.012). The number of CD31+/CD41– and CD66b+ MP subtypes, although different between protocols (p < 0.001), also increased by 41.0% ± 56.6% (p = 0.050) and 60.0% ± 53.2% (p = 0.045) following deep and repetitive breath-hold dives, respectively. Both deep and repetitive breath-hold diving lead to endothelial dysfunction that may play an important role in the genesis of neurological DCS.
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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 | 0.000 | 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 | 0.001 |
| 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".