Scuba diving and otology: a systematic review with recommendations on diagnosis, treatment and post-operative care
Bibliographic record
Abstract
Scuba diving is a popular recreational and professional activity with inherent risks. Complications related to barotrauma and decompression illness can pose significant morbidity to a diver's hearing and balance systems. The majority of dive-related injuries affect the head and neck, particularly the outer, middle and inner ear. Given the high incidence of otologic complications from diving, an evidence-based approach to the diagnosis and treatment of otic pathology is a necessity. We performed a systematic and comprehensive literature review including the pathophysiology, diagnosis, and treatment of otologic pathology related to diving. This included inner, middle, and outer ear anatomic subsites, as well as facial nerve complications, mal de debarquement syndrome, sea sickness and fitness to dive recommendations following otologic surgery. Sixty-two papers on diving and otologic pathology were included in the final analysis. We created a set of succinct evidence-based recommendations on each topic that should inform clinical decisions by otolaryngologists, dive medicine specialists and primary care providers when faced with diving-related patient pathology.
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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".