Establishing best practice recommendations for systemic treatment regimen development and maintenance.
Bibliographic record
Abstract
40 Background: A province-wide review of oncology regimens identified discrepancies in a number of regimens in systemic treatment computerized prescriber order entry (ST CPOE) systems. The potential patient harm from such discrepancies includes unnecessary toxicities and reduced treatment efficacy. The regimen review highlighted the need for a high-quality process to improve the safety of systemic treatment prescribing in Ontario. The objective of this work was to develop recommendations on best practices for the development and maintenance of oncology regimens. Methods: An expert multidisciplinary group of oncology clinicians and administrators was formed to review available literature and leverage their expertise to establish oncology-specific recommendations. These were then circulated to broader stakeholder groups for feedback and consensus. Results: Practical, consensus-based best practice recommendations for ST CPOE and pre-printed order regimen development and maintenance were created. Detailed processes for new regimen development are outlined in the table below. Moreover, broad areas of roles and responsibility, frequency of review, and sign-off were highlighted. This was repeated for regimen changes (not shown). Conclusions: There is a lack of guidance in the literature on best practices for oncology regimen development and maintenance. Careful analysis and application of the expertise of oncology professionals resulted in consensus-based best practice recommendations that will enable the advancement of safe, standardized, systemic treatment prescribing.[Table: see text]
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 | 0.135 | 0.296 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".