Bibliographic record
Abstract
Over the past 20 years, the lens of critical care research has widened, with 60 or 90 days becoming an increasingly common end point for observation of mortality and other outcomes.1–6 There have also been an increase in studies that follow patients for many years to understand how and when recovery occurs and whether risks attributable to critical illness may diminish.7–9 Correspondingly, when new therapies aimed at improving outcomes are studied, there is increasing recognition that reporting of short-term (ie, hospital) outcomes is not enough and that evaluation of the long-term effects of the intervention can be very important. Dinglas et al present long-term follow-up data of patients enrolled in the Statins in Acutely Injured Lungs in Sepsis (SAILS) trial, which examined the use of rosuvastatin in patients with sepsis-associated acute respiratory distress syndrome (ARDS).10 The SAILS trial randomised patients within 48 h of enrolment to receive either rosuvastatin or placebo for a maximum of 28 days (or until the third day after discharge from the intensive care unit (ICU), hospital discharge or death). The primary outcome of the original study was 60-day in-hospital mortality. As part of the planned analysis, the authors evaluated the long-term outcome of these patients to determine the safety of in-hospital statin use in survivors of sepsis-associated ARDS.11 The rationale was that there are known adverse effects of statins on skeletal muscle and psychological symptoms that can occur, and critically ill patients represent a population already at risk of physical and psychological difficulties. The authors collected detailed data on physical performance and psychological symptoms at 6 and …
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 | 0.321 | 0.769 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.017 |
| Bibliometrics | 0.015 | 0.020 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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; the direct Gemma label and the distilled Codex classifier 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".