What does mainstream media say about enzyme replacement therapies?
Bibliographic record
Abstract
INTRODUCTION: Enzyme replacement therapies (ERTs) are expensive drugs that can be used to treat certain inherited diseases. ERTs are not universally covered across provinces and costs are beyond the means of most patients. Media reports are commonly used to lobby for provincial ERT funding for specific patients. As physicians may be confronted with these media reports by patients, this study explored medical reporting regarding ERTs in print media. METHODS: Canadian Newsstream database was searched for articles about three ERTs-Elaprase™, Naglazyme™ and Vimizim™. Articles meeting inclusion criteria were reviewed for data regarding efficacy and adverse events, mention of role of health care professionals and medical information sources. Thematic analysis explored how efficacy was described within the articles. Data from product monographs and recent meta-analyses served as a basis for comparison. RESULTS: Of 57 articles retained for the study, 9% mentioned clinical trial data regarding drug efficacy; 7% mentioned adverse events. Only 23% of opinions about medical necessity or efficacy of the drug were from a physician. The majority were those of politicians. Information describing the condition was accurate in 90% of cases, although usually incompletely. DISCUSSION: Incomplete or inaccurate reporting about efficacy and safety may influence families that appear to be candidates for ERT. Poor reporting of medical information may also influence the social pressures placed on the government and affect funding approval for these drugs. Physicians should be aware that their patients may be exposed to misleading information.
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 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 | 0.013 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".