Current perspectives on integrating biosimilars in oncology practice in Canada
Bibliographic record
Abstract
<p class="p1"><span class="s1">B</span><span class="s2">iologic medicines have been increasingly used in oncology treatment across Canada. However, their high development and manufacturing costs place a significant economic burden on healthcare systems (Godman et al., 2018; Cornes et al., 2012). Biosimilars are highly similar versions of an original biologic medicine, demonstrating the same safety, efficacy, and immunogenicity profiles as the reference biologic medicine (Barbier et al., 2019; European Medicines Agency, 2017). Biosimilars are less costly than their reference medicines due to the ability to rely on previously completed clinical trials, which leads to a shorter development and approval process. This cost saving allows for improved patient access to biologic treatment and increased treatment options for both healthcare providers and patients (</span><span class="s3">Simon, Kucher & Partners</span><span class="s2">, 2016).<span class="Apple-converted-space"> </span></span>
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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 teacher head, 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".