Bibliographic record
Abstract
Body: The Australian Clinical Dosimetry Service (ACDS) audits every radiotherapy provider in Australia and nearly half of New Zealand. The ACDS maintains an active development program advised via consultation with the Trans-Tasman Radiation Oncology Group (TROG) and the ACDS' Clinical Advisory Group (CAG). Over the last few years the ACDS has developed and deployed IMRT, VMAT and FFF audits. For 2019-20, the focus has moved onto small field and SABR, which are now in active field trial around the country. An increasing challenge for the ACDS is how to provide coverage for standard linacs, but also how to provide audits for nonstandard and new treatment technologies. Purpose: The ACDS' three-level audit program provides a comprehensive audit service encompassing common clinical practice. A constant decision point for the ACDS is where development resources should be applied to optimally mitigate treatment risk. The ACDS and CAG constantly review audit development for both existing but less common treatment technologies, and those technologies which are expected to enter the clinical space in the near future. The audit development decisions are made on the basis of the expectation of radiation risk to the treatment population, and available resource.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.797 | 0.662 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".