Why Partial Liquid Ventilation Did Not Fulfill Its Promise
Bibliographic record
Abstract
With regard to the recent article byKacmarek and colleagues, we are in complete agreement on the absolute necessity of making public the results of negative trials (1). Discovering the reason(s) why randomized controlled trials did not confirm the information on which they were based can be highly instructive. Partial liquid ventilation (PLV), once introduced in the clinical arena, failed to live up to expectations. These disappointing results of a highly promising technique occurred in the context of major improvements in the outcome of control patients treated with conventional mechanical ventilation (CMV) utilizing a lung-protective strategy. Others have speculated that high-dose perfluorocarbon administration in the LiquiVent trial may have resulted in the socalled baby lung effect (2). We believe that the initial enthusiasm about PLV was based on a too hasty extrapolation from total liquid ventilation (TLV) data and on a poorly adapted study design used to investigate the effectiveness of PLV. In 1991, the scientific community welcomed PLV as a major technical simplification of TLV, and it was assumed that the new hybrid method would preserve all the obvious benefits of TLV. Gradually, however, we learned that the physiology of PLV is fairly complicated and definitely different from that of TLV. The lung-protective effect of PLV turned out to be less important than that associated with TLV. It took several years before the effect of perfluorocarbon liquid volume and the gas ventilation strategy to be used during PLV were investigated in depth. Strange to say, initially, most investigators used a study design adopted from surfactant research to assess PLV efficacy. In animals with respiratory failure, no attempt was made to adjust or optimize the ventilation settings. In a Medline (1966–December 2001) and personal file search, we identified 50 animal studies likely overestimating the benefits of PLV because the control groups were ventilated inadequately in terms of modern standards (3). As a consequence of inappropriate study design, much effort, time, and money have been wasted. When PLV was compared with other recruitment strategies, perfluorocarbons did not prove to be the golden bullet (4, 5), even after optimizing the gas ventilation strategy (6). We hope that PLV researchwill not be perceived as a frustrating misadventure to be forgotten as soon as possible, but rather as an instructive experience not bearing repetition.
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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 | 0.117 | 0.219 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.023 | 0.028 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".