Injury Rate in a Helicopter Underwater Escape Trainer (HUET) from 2005–2012
Bibliographic record
Abstract
BACKGROUND: Helicopter underwater escape training (HUET) carries a potential for injury and this paper identifies the injury rate. METHOD: A marine survival training school registry of all students trained between 2005-2012 in HUET and the coinciding accident records were examined. RESULTS: There were 8902 students trained in a helicopter underwater escape simulator for a total of 59,245 underwater escapes. There were 40 cases where only first-aid was required and 3 serious injuries (a laceration requiring 5 sutures, 1 dislocated shoulder, and 1 water aspiration requiring hospitalization). There were no deaths and no problems reported with using the Emergency Breathing System (EBS) or Air Pocket. Of the injuries, 11 were due to the student using a poor escape technique. The overall probability of injury was 0.74 per 1000 ascents. CONCLUSIONS: In HUET training, there is a very low injury rate with almost no significant severity. Although not scientifically proven, this would suggest that the low incident rate is due to good medical screening and the attention given by instructors to anxious students. Compared to other training such as diving, parachute jumping, and submarine escape training, the rate of injury was considerably lower.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".