Idiopathic pulmonary fibrosis - novel approach on future treatment
Bibliographic record
Abstract
IntroductionIdiopathic pulmonary fibrosis (IPF) is a fatal pulmonary disease that leads to progressive fibrosis and extremely poor resaults.. Since the etiology is unknown, there are highly limited options of the IPF treatment. The researchers are trying to discover the most valuable targets, leading them to the agents registered in different conditions or not registered as any other treatment. This innovative approach can result in IPF being determined as not fatal. PurposeThe purpose of our review is to present possible future treatment of idiopathic pulmonary fibrosis and point out the promising targets that could lead the researchers to the development of better IPF management. Materials and methodsWe have reviewed the literature from the PubMed database searching for clinical trials, meta analysis and randomized controlled trials from the past 5 years. The keywords we agreed on offered us the most informative articles and made us hope for the further development of our article. ResultsOur review shows that there are new targets that could significantly benefit IPF treatment. However, the means we presented in our review need more research to prove its safeness, effectiveness in slowing down the decline of the FVC, improving patients’ physical efficiency, their saturation level and most importantly their ability to stop the continuous fibrosis of the lungs. ConclusionsThe only treatment registered for IPF are nintedanib and pirfenidone, but the researchers continue the exploration of new possible measures to improve the survival rate and quality of life of the patients suffering from this fatal disease.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".