Patterns of symptom control and palliative care-focused original research articles in the International Journal of Radiation Oncology *Biology* Physics and the Radiotherapy and Oncology Journal, 2005–2014
Bibliographic record
Abstract
BACKGROUND: A significant portion of radiation treatment (30-40%) is delivered with palliative intent. Given the frequency of palliative care (PC) in radiation oncology, we determined the patterns of research focusing on symptom control and palliative care (SCPC) in two prominent radiation oncology journals from 2005-2014. METHODS: Original research manuscripts published from 2005-2014 in the International Journal of Radiation Oncology *Biology* Physics (Red Journal) and the Radiotherapy and Oncology Journal (Green Journal) were reviewed to categorize articles as PC and/or SCPC. Articles were categorized as PC if it pertained to any aspect of treatment of metastatic cancer, and as SCPC if symptom control in the metastatic cancer setting was the goal of the research inquiry and/or any domain of palliative clinical practice guidelines was the goal of research inquiry. RESULTS: From 2005-2014, 4.9% (312/6,386) of original research articles published in the Red Journal and 3.5% (84/2,406) published in the Green Journal pertained to metastatic cancer, and were categorized as PC. In the Red Journal, 1.3% (84/6,386) of original research articles were categorized as SCPC; 1.3% (32/2,406) of articles in the Green Journal were categorized as SCPC. There was no trend observed in the proportion of SCPC articles published over time in the Red Journal (P=0.76), the Green Journal (P=0.48), or both journals in aggregate (P=0.38). CONCLUSIONS: Despite the fact that palliative radiotherapy is a critical part of radiation oncology practice, PC and SCPC-focused original research is poorly represented in the Red Journal and the Green Journal.
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 | 0.022 | 0.151 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.052 | 0.051 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".