Prognostic Factors Influencing Survival and a Treatment Pattern Analysis of Conventional Palliative Radiotherapy for Patients with Bone Metastases
Bibliographic record
Abstract
BACKGROUND: Treatment indication for bone metastases is influenced by patient prognosis. Single-fraction radiotherapy (SFRT) was proven equally effective as multiple fractionation regimens (MFRT) but continues to be underused. OBJECTIVE: Primary objectives: (a) to identify prognostic factors for overall survival and (b) to analyze treatment patterns of palliative radiotherapy (proportion of SFRT indication and predictive factors of radiotherapy regimen) for bone metastases. METHODS: 582 patients with bone metastases who underwent conventional radiotherapy between January 1st 2014-31 December 2017 were analyzed. The Cox proportional hazard model was used to identify predictors of overall survival. For the treatment pattern analysis, 677 radiotherapy courses were evaluated. The logistic regression model was used to identify potential predictors of radiotherapy regimen. RESULTS: The 3-year overall survival was 15%. Prognostic factors associated with poor overall survival were multiple bone metastases [hazard ratio (HR = 5.4)], poor performance status (HR = 1.5) and brain metastases (HR = 1.37). SFRT prescription increased from 41% in 2017 to 51% in 2017. Predictors of SFRT prescription were a poor performance status [odds ratio (OR = 0.55)], lung (OR = 0.49) and urologic primaries (OR = 0.33) and the half-body lower site of irradiation (OR = 0.59). Spinal metastases were more likely to receive MFRT (OR = 2.09). CONCLUSIONS: Based on the prognostic factors we identified, a selection protocol for patients candidates for palliative radiotherapy to bone metastases could be established, in order to further increase SFRT prescription in our institution.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".