Treatment approaches for Multiple Myeloma vertebral column lesions – Results from an international survey distributed to the AO Spine Knowledge Forum Tumor
Bibliographic record
Abstract
Introduction: Vertebral fractures and epidural compression are common complications in Multiple Myeloma (MM). Although non-surgical management is generally preferred, internationally accepted management guidelines are lacking. This study aimed to assess current international treatment approaches and clinical conditions guiding decision-making in MM vertebral lesions. Research question: Assessing international treatment standards for MM vertebral column lesions. Material and methods: A survey was distributed to members of the AO Spine Knowledge Forum Tumor, an expert forum specialized on the treatment of oncologic spine disease. The survey consisted of 25 questions, of which 15 assessed the participant's background, clinical expertise, and experienced treatment standards regarding MM vertebral lesions, followed by ten fictional case examples with seven possible treatment scenarios each. Results: 51 international experts completed the survey, 51 % being of orthopedic, and 44 % of neurosurgical background, while 5 % were radio-oncologists. 84 % of the participants stated they "see vertebral lesions in MM in general as a non-surgical disease". As strongest indicators to perform surgery, neurological deficits (74 %), and potentially unstable lesions (20 %) were chosen. Clinical and radiological follow-up is performed by 83 %, however only in 46 % at defined intervals. 89 % would choose "less invasive" surgical strategies in MM than in similar lesions related to metastatic spine disease. Discussion and conclusion: The participating experts agreed towards a more restrained and less invasive management of MM patients, however the applicability of surgical scores, standards for follow-up, and indications as well as surgical strategies for MM vertebral lesions varied widely, illustrating the need for international guidelines standardizing treatment.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".