Tools for accurate tidal volume calculation during out-of-hospital ventilation
Bibliographic record
Abstract
Background: Out-of-hospital cardiac arrest in England affects approximately 60 000 individuals annually, with only 8% surviving to discharge. Accurate tidal volume calculation, based on predicted body weight, is essential to avoid hyperventilation and its associated risks. Aims: This systematic literature review aims to evaluate tools, which estimate height or weight in adults, to determine their suitability for use within prehospital settings, enabling accurate tidal volume calculation. Methods: A systematic literature review was conducted using MEDLINE and CINAHL databases to identify relevant studies on height and weight estimation tools. The review adhered to PRISMA guidelines and assessed study quality using a modified Newcastle-Ottawa Scale. Findings: In the prehospital care setting, three tools – ulna length, tidal tape, and the Modified PAWPER XL-MAC-2 – demonstrated good accuracy for weight estimation, with the Modified PAWPER XL-MAC-2 tool identified as the most likely to yield accurate results given the specific circumstances of prehospital care. Conclusion: Accurate height and weight estimation tools are essential for calculating tidal volumes in the management of out-of-hospital cardiac arrest. While the Modified PAWPER XL-MAC-2 appears effective, further research is needed to confirm its efficacy and practicality in prehospital settings.
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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 | 0.023 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".