Effect of Emergency Department Mattress Compressibility on Chest Compression Depth Using a Standardized Cardiopulmonary Resuscitation Board, a Slider Transfer Board, and a Flat Spine Board
Bibliographic record
Abstract
INTRODUCTION: Cardiopulmonary resuscitation (CPR) performed on a mattress decreases effective chest compression depth. Using a CPR board partially attenuates mattress compressibility. We aimed to determine the effect of a CPR board, a slider transfer board, a CPR board with a slider transfer board, and a flat spine board on chest compression depth with a mannequin placed on an emergency department mattress. METHODS: The study used a cross-over study design. The CPR-certified healthcare providers performed 2 minutes of compressions on a mannequin in five conditions, an emergency department mattress with: (a) no hard surface, (b) a CPR board, (c) a slider transfer board, (d) a CPR board and slider transfer board, and (e) a flat spine board. Compression depths were measured from two sources for each condition: (a) an internal device measuring sternum-to-spine compression and (b) an external device measuring sternum-to-spine compression plus mattress compression. The difference of the two measures (ie, depleted compression depth) was summarized and compared between conditions. RESULTS: A total of 10,203 individual compressions from 10 participants were analyzed. The mean depleted compression depths (percentage depletion) secondary to mattress effect were the following: 23.6 mm (29.7%) on a mattress only, 13.7 mm (19.5%) on a CPR board, 16.9 mm (23.1%) on a slider transfer board, 11.9 mm (17.3%) on a slider transfer board plus backboard, and 10.3 mm (15.4%) on a flat spine board. The differences in percentage depletion across conditions were statistically significant. CONCLUSION: Cardiopulmonary resuscitation providers should use a CPR board and slider transfer board or a flat spine board alone because these conditions are associated with the smallest amount of mattress compressibility.
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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".