MultiAgency, prospective, exploratory, non-intervention, cohort Study on Human Impact Exposure oNboard high-speed boats (MASHIEN): protocol
Bibliographic record
Abstract
INTRODUCTION: High-speed boat operations expose personnel to slamming-induced impacts, which can lead to musculoskeletal injuries and cognitive impairments. Despite existing safety measures, regulations and protocols, the risk of injuries remains significant. The MultiAgency, prospective, exploratory, non-intervention, cohort Study on Human Impact Exposure oNboard high-speed boats study aims to investigate the nature and magnitude of these impacts, their acute and long-term health effects, and potential injury prevention strategies to improve operational safety and performance. METHODS AND ANALYSIS: This is an ongoing multicentre, prospective, non-intervention, observational cohort study. The first participant was enrolled on 23 August 2024. High-speed boat operators log self-reported pain data via a smartphone app, using a Visual Analogue Scale and pain drawings. Triaxial accelerometers are installed on boat hulls and worn by participants to measure impact exposure. Data analysis assesses correlations between exposure and reported pain, enabling the identification of risk factors and the development of safety guidelines for high-speed boat operations. ETHICS AND DISSEMINATION: The study has received ethical approval from the relevant ethics committees, including the Swedish Ethics Review Authority (no. 2022-04931-01). All participants will provide informed consent before enrolment. The findings will be disseminated through technical reports, articles in peer-reviewed journals, conference presentations and direct engagement with military and maritime stakeholders to enhance training protocols and safety measures. TRIAL REGISTRATION NUMBER: NCT05299736.
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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 | 0.017 | 0.014 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.037 | 0.014 |
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".