An International Delphi Consensus on Defining the Optimal Surgical Composite Outcome in Metastatic Spine Disease
Bibliographic record
Abstract
STUDY DESIGN: Delphi consensus. OBJECTIVE: To define an optimal surgical composite outcome measure in patients with metastatic spine disease (OSCO-M) through international consensus among key opinion leaders. MATERIALS AND METHODS: Members of the AO Spine Knowledge Forum Tumor, an international group of dedicated spine oncology surgeons and oncologists, participated in a modified Delphi process between March 2023 and November 2024. The study was conducted in 2 parts. The first part aimed on identifying which outcome variables were deemed important to be included in the composite outcome. The second part focused on the definition of a successful outcome with regards to the agreed variables from Part 1. Each part consisted of a questionnaire and a consensus meeting. Consensus was achieved when a threshold of 70% agreement was reached. RESULTS: A total of 42 dedicated spine oncology surgeons and oncologists from North America, Latin America, Europe, and Asia participated. Over 87% of respondents agreed that composite measures reflect the multidimensional aspect of the surgical process more than an individual outcome variable. Most respondents (93%) agreed/strongly agreed that composite measures should be used to assess the quality of surgical care in spine oncology. Through consensus, the following three outcome variables were selected to define the OSCO-M: the absence of SAVES-V2 (Spinal Adverse Events Severity System, Version 2) grade 3 adverse events or higher within 30 days of surgery, maintaining or improving ECOG (Eastern Cooperative Oncology Group) performance status at 90 days, and being ambulatory (with or without aid) at 90 days. CONCLUSION: This is the first study defining a composite outcome measure in oncologic surgery for spinal metastases derived from an international group of key opinion leaders in spine oncology. The OSCO-M may be useful for future research in spine tumor patients and serve as a benchmark to optimize outcomes.
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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 | 0.344 | 0.219 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".