Long-term functional outcome of surgical treatment for Degenerative Cervical Myelopathy
Bibliographic record
Abstract
i.e.,steal phenomenon -BOLD-CVR are spatially associated with recurrent acute ischemic events.Methods: Patients with symptomatic SOD who had undergone a standardized BOLD-CVR as part of initial stroke work-up and at least one clinical follow-up MRI with diffusion-weighted images (DWI) sequence were included.Regions with BOLD-CVR below minus 2 standard deviations of a healthy cohort were introduced as severely impaired.The voxel-wise spatial agreement of steal phenomenon with recurrent DWI lesions was calculated.Using a multivariate Cox proportionalhazards model, the association between impaired BOLD-CVR and ischemic stroke recurrence was assessed.Kaplan-Meier survival analysis and Cox proportionalhazard model were used to assess the association between steal volume and ischemic stroke recurrence.Results: 130 patients were included.Of these, 28 had recurrent stroke.After adjustment for sex, age, history of atrial fibrillation and hypertension, reduced CVR showed a hazard ratio of 11.71 (4.38-29.76,p < 0.001) for recurrent ischemic stroke.Using a voxel-wise spatial agreement, 80.31% of recurrent ischemic lesions lied within voxels exhibiting steal phenomenon (i.e., paradoxical BOLD-CVR response).Patients with steal volume >73 ml had 6.40 higher hazard ratio to suffer a recurrent acute ischemic stroke.Conclusion: In patients with symptomatic SOD, those with impaired BOLD-CVR in the affected hemisphere had an 11.71-fold increased risk for recurrent ischemic stroke events compared to those with non-impaired BOLD-CVR.Furthermore, brain areas exhibiting steal phenomenon are spatially associated with recurrent acute ischemic events. Spontaneous Intracerebral Hemorrhages
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".