The impact of COVID-19 infection on idiopathic pulmonary fibrosis mortality: a systematic review and meta-analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
COVID-19 has a negative impact on the survival of respiratory patients, especially those with interstitial lung disease. This review aims to better understand the effect of COVID-19 on patients with idiopathic pulmonary fibrosis (IPF). A systematic search of MEDLINE, PubMed, Embase, and Scopus performed from December 2019 up to July 2024 identified relevant studies. Eligibility criteria included English language, sample size ≥10 patients, COVID-19 infection, and outcome measures. Two independent reviewers assessed studies using the Newcastle-Ottawa Scale for bias and extracted data. Meta-analysis employed a random-effects model, and the Grading of Recommendations Assessment, Development and Evaluation assessed evidence quality. Outcomes considered were hospitalization, intensive care unit admission, and mortality. Of the 1541 initially identified articles, 6 high-quality studies were included. Meta-analysis revealed a 34% mortality rate [95% confidence interval (CI): 21-48%], 36% hospitalization rate (95% CI: 10-75%), and 31% intensive care unit admission rate (95% CI: 7-71%) among IPF patients with COVID-19. The certainty of evidence was low or very low due to publication bias and heterogeneity. This study underscores the elevated risk of hospitalization and death in IPF patients with COVID-19, emphasizing the vulnerability of this population. Prompt and tailored care is crucial to mitigate the impact of COVID-19 on IPF patients, necessitating proactive measures, vaccination, and comprehensive management.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.018 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it