P.098 Novalis Certification of stereotactic radiosurgery programs: methodology and current status
Bibliographic record
Abstract
Background: The Novalis Certification Program is dedicated to providing a comprehensive and independent assessment of safety and quality in stereotactic radiosurgery (SRS). Methods: The program includes an independent review of SRS program structure, adequacy of personnel resources and training, appropriateness and use of technology, program quality management, patient-specific quality assurance and equipment quality control. Centres applying for Novalis Certification complete a self-study prior to a one-day visit by reviewers. Reviewers generate a descriptive 77-point report which is voted on by a multidisciplinary expert panel. Outcomes of reviews include mandatory requirements and optional recommendations, with the former requiring resolution prior to award of Certification. Sites undergo recertification every 4 years. Results: To date, 42 institutions have received Novalis Certification. A further 140 certification applications are pending. Two sites have been recertified, with 4 more in process this year. Analysis of review outcomes identified improved documentation of procedures as a frequent requirement, while frequent recommendations pertain to equipment/systems QA procedures and effective use of checklists/time outs. Conclusions: Novalis Certification is a unique, expanding peer review program assessing safety and quality in SRS and recognizing a high caliber of practice internationally. The standards-based approach highlights outstanding requirements and provides recommendations to enhance both new and established programs.
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 | 0.197 | 0.303 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".