Evaluation of Patient-Reported Outcome Differences by Radiotherapy Techniques for Bone Metastases in A Population-Based Healthcare System
Bibliographic record
Abstract
We assessed whether advanced RT techniques were associated with differences in patient-reported outcomes (PROs). Patients with bone metastases who completed the brief pain inventory (BPI) before and after RT were identified, and RT technique was categorized as simple (e.g., parallel opposed pair) or advanced (e.g., 3D-conformal RT (3DCRT), intensity-modulated RT (IMRT), or stereotactic ablative RT (SABR)). Pain response and patient-reported interference on quality of life secondary to pain was compared. A total of 1712 patients completed the BPI. From 2017−2021, the rate of advanced RT technique increased significantly (p < 0.001; 2.4%, 2.4%, 9.7%, 5.5%, 9.3%), with most advanced techniques consisting of IMRT, and only 7% of advanced techniques were SABR. Comparing simple vs. advanced technique, neither the complete pain response (12.3% vs. 11.4%; p = 0.99) nor the partial pain response (50.0% vs. 51.8%; p = 0.42) was significantly different. There was no significant patient-reported difference in pain interfering with general activity, mood, walking ability, normal work, relationships, sleep, or enjoyment of life. Given that there is increasing utilization of advanced RT techniques, there is further need for randomized trials to assess their benefits given the increased cost and inconvenience to patients.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".