WHAT DOES MAINSTREAM MEDIA SAY ABOUT ENZYME REPLACEMENT THERAPIES?
Bibliographic record
Abstract
BACKGROUND: Enzyme replacement therapies (ERTs) can be used to treat certain inherited diseases, many of which present in childhood. ERT drug development costs are high, as are market prices. Many provinces have special programs that provide coverage for expensive drugs; however, coverage of ERTs is not universal and the costs are beyond most patients or their families. Because of this, ERTs are often the focus of media attention, commonly to obtain provincial ERT funding for a specific patient. Pediatricians may be presented with these media reports and asked to support those trying to obtain provincial funding. OBJECTIVES: Given the increasing media pressure on those who approve funding for rare drugs, this study explored medical reporting regarding ERTs in print media. DESIGN/METHODS: Canadian Newsstream database was searched for articles about three ERTs – Elaprase, Naglazyme and Vimizim. Articles fulfilling inclusion criteria were reviewed for: data regarding efficacy and adverse events, mention of role of health care professionals, and sources of medical information. Thematic analysis was used to explore how efficacy was described within the articles. Data was also extracted from product monographs, and recent meta-analyses, where possible to serve as a basis for comparison. Descriptive statistics were completed where appropriate. RESULTS: Of 101 articles reviewed, 57 articles were included in the study. Nine percent of articles alluded to or reported data from clinical trials about drug efficacy; 7% of articles mentioned adverse events. Where opinion was obtained on the medical necessity or efficacy of the drug, only 23% were quoted from a physician. Opinion was most commonly obtained from a politician. Physicians were mentioned in only 49% of the articles. Roles filled by physicians were reported to include patient care/diagnosis, approval of the drug, or advocating for due process. CONCLUSION: Reporting about the efficacy and safety of ERTs was often incomplete or inaccurate. This study is of importance as mainstream media reporting may impact perceptions of families with rare diseases presenting to pediatricians, family doctors or genetics clinics who may be candidates for ERT. Additionally, incorrect reporting of medical information may influence the social pressures placed on the government and possibly the funding approval of these drugs. Physicians should be aware of the misleading information their patients are exposed to.
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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 | 0.017 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".