Development of a test protocol to evaluate infant CPR training manikins
Bibliographic record
Abstract
The lack of well-documented open-source test protocols for the evaluation of cardiopulmonary resuscitation (CPR) training manikins is an important issue for the assessment of CPR training manikins. Test protocols provide reliable information for validating manikin models. Thus, the objective of this work is to create a cross-platform, open-source LabVIEW program for the assessment of the manikin thoracic force-displacement response and force and chest-depth feedback mechanism, and to propose a test protocol that uses an Universal Testing Machine (UTM). Results of the implementation of the test protocol on an infant CPR training manikin with hysteretic thoracic response and real-time feedback mechanism are also presented. Thoracic force-displacement response is evaluated by calculating the root-mean square error (RMSE) between experimentally obtained and theoretically desired curves. Using the proposed test protocol, the infant CPR training manikin was quantitatively evaluated. Indeed, for the evaluated manikin, the compression, decompression and final score are found to be ±17 N, ±83 N and ±50 N respectively, implying that the compression phase was close to the desired curve while the decompression phase was not.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".