Risk Factors for the Development of Neurological Deficits in Metastatic Spinal Disease: An International, Multicenter Delphi Study
Bibliographic record
Abstract
STUDY DESIGN: Delphi study. OBJECTIVE: The objective of this study was to identify risk factors associated with the development and/or progression of neurological deficits in patients with metastatic spinal disease. METHODS: A three-round Delphi study was conducted between January-May 2023 including AO Spine members, comprising mainly neurosurgeons and orthopedic surgeons. In round 1, participants listed radiological factors, patient characteristics, tumor characteristics, previous cancer-related treatment factors and additional factors. In round 2, participants ranked the factors on importance per category and selected a top 9 from all factors. Kendall's W coefficient of concordance was calculated as a measure of consensus. In the final round, participants provided feedback on the rankings resulting from round 2. Lastly, the highest-ranking factors were more clearly defined and operationalized by an expert panel. RESULTS: Over two hundred physicians and researchers participated in each round. The factors listed in the first round were collapsed into 12 radiological factors, 14 patient characteristics, 6 tumor characteristics and 12 previous cancer-related treatment factors. High agreement was found in round 3 on the top-half lists in each category and the overall top 9, originating from round 2. Kendall's W indicated strong agreement between the participants. 'Epidural spinal cord compression', 'aggressive tumor behavior' and 'mechanical instability' were deemed most influential for the development of neurological deficits. CONCLUSION: This study provides factors that may be related to the development and/or progression of neurological deficits in patients with metastatic spinal disease. This list can serve as a basis for future directions in prognostication research.
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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 | 0.028 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".