Year in review 2014: Interstitial lung disease, physiology, sleep and ventilation, acute respiratory distress syndrome, cystic fibrosis, bronchiectasis and rare lung disease
Bibliographic record
Abstract
2014 was a seminal year for interstitial lung disease (ILD) with the announcement of three positive phase III clinical trials assessing treatments for the most devastating of the ILDs, idiopathic pulmonary fibrosis (IPF).1,2 Both pirfenidone and nintedanib were shown to be effective in slowing the progression of IPF with the consequence that both drugs have now been approved by the Federal Drugs Administration in the United States.While nintedanib is only now starting to be prescribed for patients, pirfenidone has been licensed in Europe since 2011 and Japan since 2008.3 With these new treatments, real-world experience of prescribing together with long-term safety studies will be important in defining the true costs and benefits of these first effective anti-fibrotic drugs.With this in mind, Valeyre et al. published an informative analysis of all patients treated to date in the CAPAC-ITY studies 4 and the open-label, rollover RECAP studies.5 This cohort included 789 subjects with a median pirfenidone exposure of 2.6 years (range 7 days to 7.7 years) and a cumulative exposure of 2059 patient-years.The adverse events noted mirrored those seen in shorter clinical trials with upper gastrointestinal disturbance and photosensitive rash predominating.Minor derangement of liver function tests was observed in 2.7% of the study population.Reassuringly, no hitherto unrecognized, rare adverse events were identified in this study population.The advent of effective therapies for IPF, a disease that had until recently been considered by many to be untreatable, has led to a considerable increase in clinical trial activity targeted at identifying novel approaches to inhibiting fibroproliferation.6 Chambers et al. reported a phase 1b trial of placentaderived mesenchymal stromal cells (MSC) in IPF.7 Eight subjects received escalating doses of MSC administered via a peripheral vein.In general, the treatment was well-tolerated and appeared to be safe.The study was not powered to determine efficacy, but at 6 months all subjects remained alive with stable disease.As noted by Glassberg and Toonkel in the accompanying editorial, the study raises at least as many questions as it answers but it has, at least, paved
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.056 | 0.015 |
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".