Bibliographic record
Abstract
Quality of Life (QoL) information regarding oncology drugs is important for patients, especially those who are receiving treatment for symptom control and not for curative purposes. Health Canada guidance documents do not describe how QoL information should be reported. This study examines how Health Canada reports QoL in documents regarding the decision to approve and indications for new oncology drugs. A list of all oncology drugs approved by Health Canada from 2019 to 2023 was created using a Health Canada website. Documents describing why a decision was made to approve a new drug and how QoL influences indications for the drug were searched for the term “Quality of Life” and relevant passages were recorded verbatim. Health Canada approved 60 oncology drugs. QoL only influenced approval in 1 case and was only mentioned in a drug’s indication in 3 cases. Health Canada only reports QoL information very infrequently for oncology drugs. • Quality of Life (QoL) measures for new oncology drugs are important for patients. • Health Canada reported how QoL influenced oncology approvals in 1 out of 60 drugs. • Health Canada reported on QoL indications in 3 out of 60 drugs. • Other regulatory authorities report on QoL more frequently than Health Canada.
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 | 0.009 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".