Reply to Camporota <i>et al.</i> : The 4DPRR Index and Mechanical Power: A Step Ahead or 4 Steps Backward?
Bibliographic record
Abstract
recruitment, increases lung homogeneity, or alters dead space).The mechanical effects of PEEP on mortality hazard may be more complex.Indeed, although PEEP's effect on total lung stress and strain will depend on multiple factors (e.g., baseline compliance, recruitability, and lung homogeneity), its amount is not indifferent to the outcome because PEEP can influence driving pressure (for a given VT) and dead space (and can indirectly influence the respiratory rate) as well as acting independently as a key component of the total mechanical energy delivered.The relevance of PEEP in determining total stress and strain of the respiratory system is, in one sense, intuitive: omitting PEEP from a calculation of energy would imply that applying 30 cm H 2 O of PEEP to an individual patient adds no risk of VILI or other adverse outcomes.On the contrary, it is clear from the univariate, population-based models presented by Costa and colleagues (1) that PEEP, the static elastic component of mechanical power and of total power, impacts mortality with an effect size of similar magnitude as respiratory rate and driving pressure.There are not sufficient data available to fully elucidate the effect of PEEP on outcome, but there is already evidence-some from the same authors-that mechanical power is associated to outcome in the same populations (4).Second, the simplicity of the bedside calculation of 4DPRR is not superior to the simplicity of the bedside calculation of mechanical power through simplified formulas (5).In addition, the 4DPRR formulation obscures the conceptual understanding of the delivered mechanical energy.Therefore, we argue that moving from the physical and physiological model of mechanical power to a contrived expression based on statistical models devoid of direct physical meaning may be a retrograde step.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.041 | 0.051 |
| Insufficient payload (model declined to judge) | 0.007 | 0.009 |
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".