Bibliographic record
Abstract
Professor Luciano Gattinoni's contributions to critical care medicine transformed the management of ARDS and mechanical ventilation, shaping the foundation of modern intensive care. Among his landmark achievements, the so-called baby lung concept redefined ARDS as a condition characterized by reduced functional lung volume, rather than lung stiffness, leading to the development of lung-protective ventilation strategies that prioritize minimizing ventilator-induced lung injury. His work on positive end-expiratory pressure advanced the understanding of lung aeration, atelectasis, and recruitment, highlighting the role of CT imaging in respiratory research. His research on prone positioning elucidated its physiologic benefits and demonstrated its lifesaving potential for patients with severe ARDS, culminating in its widespread adoption. Additionally, his work on mechanical power provided a unifying framework for assessing ventilator-induced lung injury risk, although challenges in its bedside application remain. Through his relentless pursuit of integrating respiratory physiology into clinical practice, Professor Gattinoni inspired generations of clinicians and researchers, leaving an indelible legacy that continues to guide advancements in critical care worldwide.
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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.007 | 0.015 |
| 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".