F <scp>ifty</scp> Y <scp>ears of</scp> R <scp>esearch in</scp> ARDS. Long-Term Follow-up after Acute Respiratory Distress Syndrome. Insights for Managing Medical Complexity after Critical Illness
Bibliographic record
Abstract
Critical illness is not a discrete disease state or syndrome. It is the culmination of a multiplicity of heterogeneous disease states and their varied health trajectories leading to extreme illness that requires advanced life support in a distinct geographic location in the hospital. It is a marker of newly acquired or worsened medical complexity and multimorbidities. Fifty years ago, distinguished critical care colleagues identified a syndrome of severe lung injury that united a group of patients with disparate admitting diagnoses. Acute respiratory distress syndrome continues to represent an important, incremental insult and risk modifier of acute and longer-term outcome, but it does not solely define our patients or their outcomes in isolation. Over the next 50 years, our research and clinical agenda needs to sharpen our lens on the fundamental importance of our patients' pre-critical illness health status, their intrinsic susceptibilities to tissue injury, and their innate and varied resiliencies. We need to take responsibility for the contribution that we make to morbidity through our practice in the intensive care unit each day. Engagement in frank and transparent communication with our patients and their caregivers about the very real and morbid consequences of being this sick is essential. We must enforce explicit consent about the morbidity of innovative, experimental, or high-risk medical and surgical procedures and ensure that our ongoing level of treatment aligns with patients' and caregivers' goals and values. Interprofessional and multidisciplinary collaboration is crucial to modify existing complex care pathways for our patients and their families to foster optimal rehabilitation and reintegration into the workplace and community.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.136 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.015 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".