Factors Influencing Surgical Decision-Making in the Posterior Laminectomy With Fixation for Degenerative Cervical Myelopathy (POLYFIX-DCM) Trial: Survey Study
Bibliographic record
Abstract
BACKGROUND: Degenerative cervical myelopathy (DCM) is estimated to affect 2% of the adult population. DCM occurs when degenerative processes cause compression and injure the spinal cord. Surgery to remove the stress caused by the compression of the spinal cord is the mainstay of treatment, with a range of techniques in use. Although various factors are described to inform the selection of these techniques, there needs to be more consensus and limited comparative evidence. OBJECTIVE: The main objective of this survey was to explore the variation of practice and decision-making, with a focus on laminectomy versus laminectomy and fusion in posterior surgery of the cervical spine. We present the results of a survey conducted among the principal investigators (PIs) of the National Institute for Health and Care Research (NIHR) randomized controlled trial on posterior laminectomy with fixation for degenerative cervical myelopathy (POLYFIX-DCM). METHODS: A series of 7 cases were shared with 24 PIs using SurveyMonkey. Each case consisted of a midsagittal T2-weighted magnetic resonance imaging and lateral cervical x-rays in flexion and extension. Surgeons were asked if their preferred approach was anterior or posterior. If posterior, they were asked whether they preferred to instrument and whether they had the equipoise to randomize in the NIHR POLYFIX-DCM trial. Variability in decision-making was then explored using factors reported to inform decision-making, such as alignment, location of compression, number of levels operated, presence of mobile spondylolisthesis, and patient age. RESULTS: The majority of PIs (16/30, 53%) completed the survey. Overall, PIs favored a posterior approach (12/16, 75%) with instrumentation (75/112, average 66%) and would randomize (67/112, average 62%) most cases. Factors reported to inform decision-making poorly explained variability in responses in both univariate testing and with a multivariate model (R2=0.1). Only surgeon experience of more than 5 years and orthopedic specialty training background were significant predictors, both associated with an anterior approach (odds ratio [OR] 1.255; P=.02 and OR 1.344; P=.007, respectively) and fusion for posterior procedures (OR 0.628; P<.001 and OR 1.344; P<.001, respectively). Surgeon experience also significantly affected the openness to randomize, with those with more than 5 years of experience less likely to randomize (OR -0.68; P<.001). CONCLUSIONS: In this representative sample of spine surgeons participating in the POLYFIX-DCM trial as investigators, there is no consensus on surgical strategy, including the role of instrumented fusion following posterior decompression. Overall, this study supports the view that there appears to be a clinical equipoise, and conceptually, a randomized controlled trial appears feasible, which sets the scene for the NIHR POLYFIX-DCM trial.
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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 | 0.023 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".