Recreational diving in persons with type 1 and type 2 diabetes: Advancing capabilities and recommendations
Bibliographic record
Abstract
Diving by persons with diabetes has long been conducted, with formal guidelines published in the early 1990s. Subsequent consensus guidelines produced following a 2005 workshop helped to advance the recognition of relevant issues and promote discussion. The guidelines were intended as an interim step in guidance, with the expectation that revisions should follow the gathering of additional data and experience. Recent and ongoing developments in pharmacology and technology can further aid in reducing the risk of hypoglycemia, a critical acute concern of diving with diabetes. Careful and periodic evaluation remains crucial to ensure that participation in diving activity is appropriate. Close self-monitoring, thoughtful adjustments of medications and meals, and careful review of the individual response to diving can assist in optimising control and ensuring safety. Open communication with diving partners, support personnel, and medical monitors is important to ensure that all are prepared to effectively assist in case of need. Ongoing vigilance, best practice, including graduated clearance for diving exposures and adverse event reporting, are all required to ensure the safety of diving with diabetes and to promote community understanding and acceptance.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".