Rebuttal from Jeremy R. Beitler, Rolf D. Hubmayr and Atul Malhotra
Bibliographic record
Abstract
More than a decade after low tidal volume ventilation for acute respiratory distress syndrome (ARDS) first gained widespread acceptance, it is still unknown how best to manage its effects on minute ventilation. High respiratory rate, or permissive hypercapnia? Limiting respiratory rate has been shown in preclinical models to reduce lung injury even at a constant arterial CO2 tension (; Vaporidi et al. 2008). Curley and colleagues (2013) go one step further, making the case for additional benefit from hypercapnia during low tidal volume ventilation. As we find no great fault with the balanced case put forward by our Canadian colleagues, our rebuttal focuses on challenges in designing more definitive trials. First, how might the independent effects of respiratory rate, and pH each be considered? Tris-hydroxymethyl amino-methane (THAM) buffer might be used to identify the effects of hypercapnia independent of acidaemia. To isolate the effects of respiratory rate from , inspired CO2 could be administered during high respiratory rate to induce hypercapnia to a degree comparable to a low-rate strategy. Optimizing respiratory rate must also consider airflow dynamics since high rates may lead to auto-positive end-expiratory pressure (auto-PEEP). Second, what is the minimum acceptable pH, and how should severe acidaemia be managed? A minimum pH approaching 7.15 was well tolerated haemodynamically in a heterogeneous ARDS population (Carvalho et al. 1997), while other studies specified a pH nadir between 7.05 (Brochard et al. 1998) and 7.30 (Brower et al. 2000) before encouraging intervention. THAM has shown promise as an effective buffer during fixed minute ventilation (Kallet et al. 2000), although further study is warranted before its widespread adoption as a rescue therapy. Finally, how do we ensure the protection of patients at highest risk of harm from hypercapnia? Patients with intracranial hypertension may fare poorly from hypercapnia-induced cerebral vasodilatation, as may patients with pre-existent right ventricular compromise facing hypercapnic pulmonary vasoconstriction (Curley et al. 2010). Similarly, anti-inflammatory effects of hypercapnia may be deleterious in pulmonary or extra-pulmonary sepsis compared to other ARDS precipitants. Moreover, increased sedation or paralysis, with associated risk of iatrogenic injury, may be required during hypercapnia to maintain patient–ventilator synchrony and minimize large swings in transpulmonary pressures from spontaneous breathing efforts (Malhotra & Drazen, 2013). Only with carefully designed studies will the role be defined for optimizing respiratory rate, and pH in individual patients with varying comorbidities and ARDS severity. The range of preclinical findings and heterogeneity of current clinical practice indicate a great need for further research in this area. Readers are invited to give their views on this and the accompanying CrossTalk articles in this issue by submitting a brief comment. Comments may be posted up to 6 weeks after publication of the article, at which point the discussion will close and authors will be invited to submit a ‘final word’. To submit a comment, go to http://jp.physoc.org/letters/submit/jphysiol;591/11/2773 Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article. J. R. Beitler and R. D. Hubmayr have no conflicts of interest to declare. A. Malhotra previously received consulting and/or research income from Philips, SGS, SHC, Apnex, Apnicure and Pfizer, but has relinquished all outside personal income since May 2012.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.068 | 0.078 |
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".