Frontline Voice: AO Spine Member Survey Regarding Spine Oncology Knowledge Generation and Translation Needs
Bibliographic record
Abstract
Study Designcross-sectional survey.ObjectivesTo evaluate AO Spine members' practices and comfort in managing metastatic and primary spine tumors, explore the use of decision-support and patient assessment tools, and identify knowledge gaps and future needs in spine oncology.MethodsAn online survey was distributed to AO Spine members to query comfort levels with key decisions in spinal oncology management, utilization of decision frameworks and spine oncology-specific instruments, and educational material preferences.ResultsResponses were obtained from 381 members across 82 countries. Most respondents were orthopedic spine surgeons (62%) or neurosurgeons (36%), with 42% performing 100-200 spine surgeries per year. Extradural primary and metastatic tumors were managed by 84% and 95% of respondents, respectively, with survival and frailty assessment tools used for both. While most surgeons felt comfortable determining when emergency surgery was needed (81% for primary and 82% for metastatic tumors), nuanced decisions about surgical timing were more challenging. Surgeons also noted challenges in tailoring the oncologic surgical plan to what the patient could safely tolerate. There was a strong desire for guidelines on tumor-related spinal pain (85%), treatment timing (85%), stabilization (85%), and glucocorticoid use for symptomatic extradural metastatic tumors (77%). Interest was high for classification systems for spine tumor pain (65%) and stabilization decisions (80%).ConclusionsAdditional support is needed in decision-making regarding surgical timing, patient selection, and tailoring treatment invasiveness to life expectancy and frailty. Surgeons seek further guidance to prevent neurologic deterioration and optimize recovery. Guidelines and classification systems were highly coveted for daily practice.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.007 |
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".