Population description and clinical response assessment for spinal metastases: part 2 of the SPIne response assessment in Neuro-Oncology (SPINO) group report
Bibliographic record
Abstract
Background: Approximately 40% of metastatic cancer patients will develop spinal metastases. The current report provides recommendations for standardization of metrics used for spinal oncology patient population description and outcome assessment beyond local control endpoints on behalf of the SPIne response assessment in Neuro-Oncology (SPINO) group. Methods: The SPINO group survey was conducted in order to determine the preferences for utilization of clinician-based and patient-reported outcome measures for description of patients with spinal metastases. Subsequently, ClinicalTrials.gov registry was searched for spinal oncology clinical trials, and measures for patient description and outcome reporting were identified for each trial. These two searches were used to identify currently used descriptors and instruments. A literature search was performed focusing on the measures identified in the survey and clinical trial search in order to assess their validity in the metastatic spinal tumor patient population. References for this manuscript were identified through PubMed and Medline searches. Results: Published literature, expert survey, and ongoing clinical trials were used to synthesize recommendations for instruments for reporting of spinal stability, epidural tumor extension, neurological and functional status, and symptom severity. Conclusions: Accurate description of patient population and therapy effects requires a combination of clinician-based and patient-reported outcome measures. The current report provides international consensus recommendations for the systematic reporting of patient- and clinician-reported measures required to develop trials applicable to surgery for spinal metastases and postoperative spine stereotactic body radiotherapy (SBRT).
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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 | 0.164 | 0.207 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".