Incidence of Spinal Disease and Role of Spinal Radiotherapy in Multiple Myeloma
Bibliographic record
Abstract
Background: Spinal disease (spd) in multiple myeloma (mm) can be a major source of morbidity in newly diagnosed patients and long-term survivors. We retrospectively assessed the incidence of spinal disease in patients newly diagnosed with myeloma, its effect on survival, and the possible effect of spinal radiation therapy (rt). Methods: Patients diagnosed with mm between 2010 and 2014 were identified through the provincial cancer registry. Plain radiography, computed tomography, and magnetic resonance imaging were reviewed to detect and document the type of spd. Data related to rt and systemic therapy were collected. Kaplan-Meier and time-varying Cox regression models were used to describe overall survival. Results: Of 306 identified patients with newly diagnosed mm, 51% had spd, including 17% with lytic disease, 68% with compression fractures, and 15% with spinal cord compression. Of the patients with spd, 61% received spinal rt. Of those patients, 84% received spinal rt within 3 months after their diagnosis. Median dose was 20 Gy. Most patients (89.2%) received chemotherapy, and 22.5% underwent autologous stem-cell transplantation. Only 6 of the patients treated with spinal rt received re-irradiation to the same site. Overall survival was similar for patients with and without spd. On multivariate analysis, spinal rt had no effect on survival. Conclusions: In patients newly diagnosed with mm, spd is a common presentation. With current systemic therapy, the presence of spd had no adverse effect on overall survival. The effect of spinal rt on overall survival was nonsignificant.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".