Sci‐Fri PM: Planning‐05: Saving trees and improving workflow
Bibliographic record
Abstract
Patient treatment preparation is a multi-step process requiring inputs from a range of disciplines and technologies. Our centre generates just over 4500 treatment plans annually and operates from two main campuses. A large, split program presents unique challenges during treatment preparation and has provided the impetus for a completely electronic treatment process. Throughout 2006 and 2007, we migrated our external beam treatment planning to the Computerized Medical Systems (CMS) product line. Utilizing a thin-client architecture, CMS supports distributed (multi-site) planning. Coincident with the treatment planning upgrade, IMPAC Multi-Access was configured to provide a paperless and filmless treatment record and electronic patient workflow. Standardized treatment objectives were also implemented in the form of site-group approved care plans. Details of the pretreatment process and the CMS / IMPAC implementation will be presented as well as a workflow time analysis. To date, treatment preparation times have been reduced by 25% (2.5 days) as a result of workflow improvements, representing a clear benefit to both staff and patients.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".