Measuring the Burden of Cystic Fibrosis: A Comprehensive Analysis and Scoping Review
Bibliographic record
Abstract
Cystic fibrosis (CF) is a complex and expensive rare inherited disease that affects over 4,300 Canadians. There is no cure. Between 2015 and 2020, more than 300 Canadians lost their lives to CF; the average age at death was only 34 years. CF is a progressive disease requiring arduous interventions to manage symptoms and keep people healthy for as long as possible. The annual Canadian CF Registry report highlights some of the burden experienced by Canadians with CF: for example in 2018 Canadians with CF spent 26,500 days in hospital and had 17,700 home intravenous antibiotic therapy days. Behind each of these statistics is a person who must bear the burden of this disease each and everyday and a family who cares for and supports each person living with CF. We have heard from people with CF (national survey (1500 people), patient engagement session (9 people)) that the costs, both in terms of financial cost and time lost, are enormous. With this scoping review, we set out to ask, for patients and caregivers, living with cystic fibrosis, what are the costs, including quantitative or qualitative, direct and indirect, derived from the disease that represent a holistic view of the socio-economic cost of cystic fibrosis?
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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 | 0.024 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.040 | 0.040 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".