Diving practices in technical divers’ community and behaviour towards self-reported unusual symptoms
Bibliographic record
Abstract
Introduction: The use of gas mixtures containing helium for deep recreational diving is increasingly common, involving complex logistics and decision-making compromises. The characteristics and inherent risks of this practice remain poorly documented. This study aims to provide an epidemiological inventory of practices and diving-related incidents within the technical diving community. Methods: An international online survey was disseminated on social networks targeting certified trimix divers. It collected demographic data, diving experience, and dive management practices, along with self-reported unusual symptoms, treatments, and outcomes following trimix dives. Results: A total of 558 questionnaires were analysed, predominantly from males (92%), mostly over 46 years old (61%), with high certification levels and recreational diving purposes. Forty-two percent reported one or more medical risk factors related to diving. Rebreather use was prevalent (79% at least occasionally). Decompression was primarily managed using compartmental models (85%) with gradient-factors adjustment. Dive planning varied significantly among individuals. Gas density at depth frequently exceeded the current recommendations. Ten percent had experienced symptoms suggestive of gas toxicity, mainly related to nitrogen narcosis. Thirty-six percent (199/558) reported experiencing, at least once, symptoms of diving-related incidents, with 61% (n = 121/199) expressing certainty. In 48% (120/261) of incidents involving decompression sickness (DCS) or breathing symptoms, no treatment was initiated. Among episodes involving DCS symptoms (n = 254), 42% received normobaric oxygen, and 23% sought medical advice, while 16% were treated with hyperbaric oxygen. Only 2.5% reported probable long-lasting sequelae. Conclusions: The diversity of practices highlights the lack of robust scientific data supporting them. The accident rate in mixed-gas diving may be higher than in typical scuba air diving, though mostly of mild severity. Treatment appears to be neglected despite divers' high knowledge levels. Continued research into decompression and the physiological effects of these dives is essential, along with ongoing awareness and education efforts in diving first aid within this exposed community.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".