Bibliographic record
Abstract
We read with interest Dr. Tobin's provocative comments on the ontology and epistemology of acute respiratory distress syndrome (ARDS), the validity of randomized trials, and the nature of coronavirus disease .Unfortunately, we could not understand how they relate directly to our Critical Care Perspective (1), which focused on a framework for critical care definitions.Indeed Dr. Tobin's reflections are virtually identical to those in his 2020 Commentary (2), which was written well before our article appeared.We agree that syndromes, like ARDS, sepsis, depression, heart failure, and many more-which are by definition, creations of mankind rather than of natural law-are only valuable insofar as they are useful to clinicians or researchers.We also agree that there are many more important decisions at the critical care bedside than whether a patient does or does not "have ARDS."The purpose of our editorial was not to add fuel to the fire of the decades-long debate about whether a syndrome called ARDS exists or is merely a compilation of multiple heterogeneous causes of acute hypoxemic respiratory failure (the old lumper vs. splitter debate) (3, 4).Instead, we provided a framework that we hoped would address many of the concerns related to previous definitions of ARDS (and other critical care syndromes), with a strong focus on the need to empirically test reliability, feasibility, and validity of any new definition.Finally, Dr. Tobin strongly implies that the "fetish fixation on the Berlin definition" likely contributed to "patient mortality at the height of the pandemic."In the spirit of honesty, which Tobin highlighted in his letter, it would be helpful if he provided data in support of this inflammatory statement and not simply a reference to his own Commentary, which did not address this issue (2).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.142 | 0.099 |
| Insufficient payload (model declined to judge) | 0.017 | 0.013 |
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".