Patterns of Practice in the Prescription of Palliative Radiotherapy for the Treatment of Thoracic Symptoms of Lung Cancer at the Rapid Response Radiotherapy Program between 2006 and 2012
Bibliographic record
Abstract
Objective: Radiation therapy can often be used for palliation of thoracic symptoms in patients presenting with locally advanced lung cancer or lung metastases. The aim of this study was to examine whether patterns of practice in prescription of palliative thoracic radiation therapy have changed over time in the Rapid Response Radiotherapy Program. Secondary outcomes were factors that may have influenced the treatment regimen prescribed, including patient, disease, and organisational factors. Methods: This study was a retrospective review of a prospective database of patients with locally advanced lung cancer or lung metastases referred to the Rapid Response Radiotherapy Program for thoracic symptoms between 1 July 2006 and 30 April 2012. Patient demographics, and organisational and disease factors were descriptively analysed. Differences in proportions between unordered categorical variables were examined using chi-squared test. Univariate logistic regression analysis and backward stepwise selection procedure were used to determine the most significant factors in prescription practice. Results: A total of 175 courses of palliative thoracic radiation therapy were prescribed. The median age of the patients was 71 years, and the median Karnofsky Performance Status was 60. The most commonly prescribed treatment regimen was 20 Gy in 5 fractions (20 Gy/5), which made up 64% of all the courses prescribed. There was a significant increase in frequency of the prescription of 20 Gy/5 over time (p = 0.02). The site of radiation (disease factor) and years of certification for independent practice of the treating radiation oncologist (organisational factor) were also significant factors in the prescription of 20 Gy/5 over time (both p = 0.02). Conclusion: A significant increase in the prescription of 20 Gy/5 was observed over time. However, the prescription of a higher dose fractionation schedule for patients with a higher performance status, as seen in other clinical trials and guidelines, was not observed. Future studies should further explore other possible factors such as patient survival, preference, comorbidities, and disease burden that may influence the dose fractionation prescribed.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".