Controversies with Kalydeco: Newspaper coverage in Canada and the United States of the cystic fibrosis “wonder drug”
Bibliographic record
Abstract
BACKGROUND: The cystic fibrosis drug, Kalydeco, has attracted attention both for its effectiveness in particular CF patients and its substantial price tag. An analysis of newspaper portrayals of Kalydeco provides an opportunity to examine how policy issues associated with rare diseases and orphan drugs are being represented in the popular press. METHODS: We conducted a content analysis of 203 newspaper articles in Canada and the U.S. that mention Kalydeco. Articles were analyzed for their main frame, discussion of Kalydeco, including issues of drug development, patient access, and reimbursement, and overall tone. RESULTS: In Canadian newspaper coverage, 77.4% of articles were framed as human interest stories featuring individual patients seeking public funding for Kalydeco, yet only 7.5% mentioned any budgetary limitations in doing so. In contrast, U.S. newspaper coverage was framed as a financial/economic story in 43.1% of articles and a medical/scientific story in 27.8%. CONCLUSIONS: Newspaper coverage varied significantly between Canada, where Kalydeco is predominantly a story about increasing patient access through full government funding, and the U.S., where Kalydeco is largely a financial story about the economic impact of Kalydeco. The difference in coverage may be due to differences in public funding between the healthcare systems of these two countries.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".