Bibliographic record
Abstract
Healthcare spending in Canada has grown rapidly in recent years, especially for drugs.This paper discusses the causes of the problem and makes policy proposals.Conflicts of interest (COIs) are a frequent occurrence in medical research and lead to bias.Published studies, especially in the area of clinical trials on drugs, are much more likely to produce findings favourable to the drug when funded by the manufacturer.Bias can occur by various means, including inappropriate study design (such as giving a placebo to control subjects rather than an existing drug) and selective publication of results.COIs also frequently occur with clinical practice guidelines.High-priced (particularly new) drugs are often marketed by inappropriate means.Drug costs in Canada could be greatly reduced if doctors prescribed lower-cost alternatives where appropriate (therapeutic substitution).Proposals are made for changes in the regulatory agencies responsible for the approval of drugs, drug marketing and post-marketing surveil- Vol.3 No.2, 2007 [39]lance.In addition, a new regulatory agency is proposed that would examine the value of drugs and medical devices in terms of clinical effectiveness and cost-effectiveness.Such an agency would set the rules for therapeutic substitution and would determine which medical interventions can be used based on agreed cost-effectiveness criteria. D I S C U S S I O N A N D D E B AT E HEALTHCARE POLICY RésuméLes dépenses en santé ont connu une croissance rapide au Canada au cours des dernières années, surtout pour ce qui est des médicaments.Cet article examine les causes du problème et propose des politiques.Les conflits d'intérêts sont chose courante dans la recherche médicale et entraînent des biais.Les études publiées -en particulier dans le domaine des essais cliniques portant sur les médicaments -sont beaucoup plus susceptibles de parvenir à des conclusions favorables au médicament lorsque ces études sont financées par le fabricant.Les biais peuvent se manifester de diverses façons, y compris une méthodologie inappropriée (comme, par exemple, donner aux sujets-témoins un placebo au lieu d'un médicament existant) et une publication sélective des résultats.De plus, des conflits d'intérêts surviennent fréquemment avec les lignes directrices sur la pratique clinique.De nombreux médicaments coûtent excessivement cher et sont souvent commercialisés par des moyens inappropriés.Le coût des médicaments au Canada pourraient être considérablement réduits si les médecins prescrivaient des solutions thérapeutiques moins coûteuses lorsque possible (substitution thérapeutique).On propose des changements à apporter aux organismes réglementaires responsables de l' approbation, de la commercialisation et de la surveillance post-commercialisation des médicaments.On propose également de créer un nouvel organisme réglementaire qui serait chargé d' examiner la valeur des médicaments et des appareils médicaux tant du point de vue de leur efficacité clinique que de leur rapport coût-efficacité.Un tel organisme mettrait en œuvre la substitution thérapeutique et déterminerait quelles interventions médicales peuvent être utilisées d' après les limites de dépenses convenues.
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 | 0.012 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.022 | 0.016 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".