Bibliographic record
Abstract
The Journal has been in the vanguard of publications of the respiratory microbiome, including a National Institutes of Health Workshop report, establishing the normal microbiome in patients with various risks, and in the correlation of microbiome changes with disease exacerbations and lung transplant. A new classification scheme for healthcare-associated pneumonia, risks for nosocomial Pseudomonas pneumonia, and associations between community-acquired pneumonia and risks or outcomes have been reported. The increasingly recognized role of viral respiratory tract infections was reflected in publications regarding incidence rates, risk factors, and associations with other respiratory diseases. Significant contributions to understanding and treating sepsis emerged in 2013. The role of tissue damage was highlighted in a series of publications. A much greater understanding of the importance of pathways that directly impact the pathogen at the site of infection and subsequent pathogen clearance has emerged. The Journal published important contributions across the spectrum of ineffective therapy (activated protein C), novel therapeutic ideas (statins and extracorporeal membrane oxygenation), and solidly beneficial approaches (early protocolized care). Biomarker development is maturing to include a wide array of molecular measurements increasingly aimed at aiding improved therapy.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".