Bibliographic record
Abstract
PURPOSE OF REVIEW: Tracking patient outcomes using cystic fibrosis (CF) national data registries, we have seen a dramatic improvement in patient survival. As there are multiple ways to measure survival, it is important for readers to understand these different metrics in order to clearly translate this information to patients and their families. The aims of this review were to describe measures of survival and to review the recent literature pertaining to survival in CF to capture the changing epidemiology. RECENT FINDINGS: Although survival has improved on a population level, several individual factors continue to impact survival such as sex, age of diagnosis, ethnic background and lung function. Survival estimates, conditional on surviving to a specified age, are more relevant to individuals living with CF today and are higher than the reported overall median age of survival. There is some evidence to suggest that newborn screening (NBS) has resulted in prolonged survival in CF. SUMMARY: Prognosis in CF is often described by reporting the median age of survival, the median age of death, the median survival conditional on living to a certain age and the survival by birth cohort. Each of these metrics provide useful information depending on an individual's personal disease trajectory. The median age of survival continues to increase in CF in many countries while mortality rates are decreasing. Several factors have been associated with worse survival such as female sex, ethnicity, worse nutritional status, lower lung function and microbiology. When comparing survival between countries, one needs to ensure that similar data collection and processing techniques are used to ensure valid and robust comparisons.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".