Prevalence of Palliative Radiotherapy Abstracts Presented at Annual Scientific Meetings of the Canadian Association of Radiation Oncology: 2003-2021
Bibliographic record
Abstract
Abstract Purpose.Approximately half of all radiotherapy (RT) is delivered with palliative intent. Clinical research in palliative RT aims to manage symptoms, improve quality of life (QoL), evaluate supportive care, and determine optimal dose-fractionation schedules. Our aim was to describe the prevalence of palliative research at the Canadian Association of Radiation Oncology (CARO) Annual Scientific Meeting (ASM) over time. Methods. Published abstracts (2003-2021) were independently reviewed by two authors who categorized each as: curative-intent; palliative-intent; pertaining to both populations; or neither. Abstracts were considered palliative if they described incurable malignancy and interventions primarily for symptom control or QoL. Type of study, primary, site treated, and symptoms palliated were recorded. Descriptive and summary statistics were calculated including one-way ANOVA test for trend. Results. 339/4566 abstracts (7.4%, range 2.4-13.9% per year) were classified as palliative. 7.7% (26/339) described phase I-III trials. The main primary site was lung (39/339) and the most common metastatic site was bone (34.2%). QoL, symptom and toxicity outcomes were reported in 31.6% (107/339), 37.8% (128/339) and 17.7% (60/339), respectively. The most common symptom investigated was pain (38/339). The proportion of abstracts classified as curative, palliative or reporting toxicity endpoints demonstrated significant change over time (all p<0.0001). Conclusion. While proportion of palliative themed abstracts has increased with time, there remains a significant gap before equivalence with the prevalence of palliative RT in clinical practice is achieved.
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 | 0.013 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".