SU‐F‐P‐08: Medical Physics Perspective On Radiation Therapy Quality and Safety Considerations in Low Income Settings
Bibliographic record
Abstract
Purpose: The last few years have seen a significant growth of interest in the global radiation therapy crisis. Various organizations are quantifying the need and providing aid in support of addressing the shortfall existing in many low‐to‐middle income countries (LMICs). The Lancet Oncology Commission report (Lancet Oncol. Sep;16(10):1153‐86, 2015) projects a need of 22,000 new medical physicists in LMICs by 2035 if there is to be equal access globally. With the tremendous demand for new facilities, equipment and personnel, it is very important to recognize quality and safety considerations and to address them directly. Methods: A detailed examination of quality and safety publications was undertaken. A paper by Dunscombe (Front. Oncol. 2: 129, 2012) reviewed the recommendations of 7 authoritative reports on safety in radiation therapy and found the 12 most cited recommendations, summarized in order of most to least cited: training, staffing, documentation/standard operating procedures, incident learning, communication/questioning, check lists, QC/PM, dosimetric audit, accreditation, minimizing interruptions, prospective risk assessment, and safety culture. However, these authoritative reports were generally based on input from high income contexts. In this work, the recommendations were analyzed with a special emphasis on issues that are significant in LMICs. Results: The review indicated that there are significant challenges in LMICs with training and staffing ranking at the top in terms quality and safety. Conclusion: With the recognized need for expanding global access to radiation therapy, especially in LMICs, and the backing by multiple support organizations, quality and safety considerations must be overtly addressed. While multidimensional, training and staffing are top priorities. The use of outdated systems with poor interconnectivity, coupled with a lack of systematic QA in high patient load settings are additional concerns. Any support provided to lower resourced settings must address the multiple facets associated with these quality and safety indicators.
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 | 0.016 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".