Courage, collaboration, complexity and chemotherapy safety: The view from the sharp end
Bibliographic record
Abstract
The Canadian oncology community was devastated by the news in August 2006 that a patient had died from an overdose of fluorouracil. Where we once thought our checks and balances ensured patient safety, we now knew they were not enough. Practice immediately began to change around the country. However, the incident report highlighted that there was much we still didn’t know about safety issues in intravenous ambulatory chemotherapy safety in Canada. In response, an interdisciplinary, pan-Canadian team launched an 18-month exploratory study, resulting in a report identifying several safety issues and associated recommendations. This paper summarizes the key insights we have gathered for Canadian oncology nurses in being part of this study: that we need courage to come forward and disclose safety concerns; we should collaborate to come up with safety improvements that work for everyone; and we should strive to simplify our work at the sharp end by reducing complexity upstream and throughout the system.
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 | 0.019 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.034 | 0.064 |
| Scholarly communication | 0.024 | 0.013 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.008 | 0.023 |
| Insufficient payload (model declined to judge) | 0.005 | 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".