Thoracic weighting of restrained subjects during exhaustion recovery causes loss of lung reserve volume in a model of police arrest
Bibliographic record
Abstract
Restraint asphyxia has been proposed as a mechanism for some arrest-related deaths that occur during or shortly after a suspect is taken into custody. Our analysis of the literature found that prone positioning, weight applied to the back, recovery after simulated pursuit, and restraint position have led to restrictive, but non life-threatening respiratory changes when tested in subsets. However, the combined effects of all four parameters have not been tested together in a single study. We hypothesized that a complete protocol with high-sensitivity instrumentation could improve our understanding of breathing physiology during weighted restraint. We designed an electrical impedance tomography (EIT)-based protocol for this purpose and measured the 3D distribution of ventilation within the thorax. Here, we present the results from a study on 17 human subjects that revealed FRC declines during weighted restrained recovery from exercise for subjects in the restraint postures, but not the control posture. These prolonged FRC declines were consistent with abdominal muscle recruitment to assist the inspiratory muscles, suggesting that subjects in restraint postures have increased work of breathing compared to controls. Upon removal of the weighted load, lung reserve volumes gradually increased for the hands-behind-the-head restraint posture but continued to decrease for subjects in the hands-behind-the-back restraint posture. We discuss the possible role this increased work of breathing may play in restraint asphyxia.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".