Spinal surgeon delphi panel to gain consensus on minimally invasive surgery definitions for spinal fusion
Bibliographic record
Abstract
BackgroundWhile there is a clear trend towards less invasive spinal procedures in recent years, determining what specifically constitutes minimally invasive surgery (MIS) for spinal fusion procedures can be challenging as published definitions are heterogeneous and address only select factors.PurposeTo utilize a Delphi Consensus Panel (DCP) process to build consensus among panelists regarding the definition of MIS for spinal fusion and define a consensus around best practices for adopting MIS technologies.MethodsDCP is a widely accepted method of qualitative data collection and analysis used for generating consensus from panel members. The DCP consisted of 2 online questionnaires completed by 11 expert panelists. Round 1 included 7 multiple choice demographics and 11 open-ended questions. Round 1 responses were analyzed to inform the development of statements for further assessment in Round 2. Round 2 included 93 closed-ended, 5-point Likert scale questions and 11 open-ended questions. Consensus was defined as agreement among at least 8 out of 11 panelists.ResultsConsensus was reached on 56 out of 93 statements regarding spinal fusion and/or fixation procedures. There was a high degree of consensus around the role of reduced tissue trauma, patient recovery time, hospital length of stay, and post-operative pain for MIS spinal fusion procedures compared to open procedures; and the value of various technologies in performing MIS spinal fusion procedures.ConclusionA consistent definition of what constitutes MIS for spinal fusion may help drive consistency across surgeons, facilities, and payers to define clinical and economic implications for these approaches.
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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 | 0.201 | 0.205 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".