Vibration and bubbles: a systematic review of the effects of helicopter retrieval on injured divers
Bibliographic record
Abstract
INTRODUCTION: Vibration from a helicopter during aeromedical retrieval of divers may increase venous gas emboli (VGE) production, evolution or distribution, potentially worsening the patient's condition. AIM: To review the literature surrounding the helicopter transport of injured divers and establish if vibration contributes to increased VGE. METHOD: A systematic literature search of key databases was conducted to identify articles investigating vibration and bubbles during helicopter retrieval of divers. Level of evidence was graded using the Oxford Centre for Evidence-Based Medicine guidelines. A modified quality assessment tool for studies with diverse designs (QATSDD) was used to assess the overall quality of evidence. RESULTS: Seven studies were included in the review. An in vitro research paper provided some evidence of bubble formation with gas supersaturation and vibration. Only one prospective intervention study was identified which examined the effect of vibration on VGE formation. Bubble duration was used to quantify VGE load with no difference found between the vibration and non-vibration time periods. This study was published in 1980 and technological advances since that time suggest cautious interpretation of the results. The remaining studies were retrospective chart reviews of helicopter retrieval of divers. Mode of transport, altitude exposure, oxygen and intravenous fluids use were examined. CONCLUSION: There is some physical evidence that vibration leads to bubble formation although there is a paucity of research on the specific effects of helicopter vibration and VGE in divers. Technological advances have led to improved assessment of VGE in divers and will aid in further research.
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".