Validation of a novel scoring system (Cervical Surgical Score) for the management of degenerative cervical myelopathy
Bibliographic record
Abstract
Degenerative Cervical Myelopathy (DCM) is the leading cause of spinal cord dysfunction globally. Surgical intervention is often recommended for moderate to severe cases, but the optimal surgical approach remains debated. This study aims to validate the novel Cervical Surgical Score (CSS) for managing DCM, aiding surgical decision-making. A prospective study was conducted in Carlo Besta institute (Milan) from a consecutive series, enrolling 113 patients undergoing surgery for DCM from January 2022 to February 2023. This cohort was compared with 106 patients from a retrospective cohort treated between 2019 and 2021. A total 219 patients (113 prospective, 106 retrospective) were included. The prospective group had an average age of 59.6 years (61 % males), and the retrospective group, 60.7 years (69 % males). The mean CSS score (calculated based on age, level of cervical pathologies, level of myelopathy, extension, site and type of compression, cervical alignment and mJOA) was 12.3 for prospective and 13.18 for retrospective groups. Most prospective cases used an anterior approach compared to retrospective group (88,5 % vs 48.1 %). At two years, neurological recovery (last follow-up mJOA-preoperative mJOA)/(18−preoperative mJOA × 100) was higher in prospective group (68 % vs. 54 %). CSS concordance linked to better recovery rates at one and two years (45 % and 66 % vs. 29 % and 47 %; p < 0,001). High-expertise surgeons (defined based on case-load evaluation scale) achieved higher CSS concordance (64 %) than medium (31 %) and low-expertise surgeons (0 %). The CSS is a reliable tool for optimizing surgical strategies for DCM, enhancing decision-making, and improving patient outcomes. • The CSS provides a structured approach to surgical decision-making in degenerative cervical myelopathy. • The CSS involves key clinical and radiological factors for patient-specific surgical recommendations. • Adherence to CSS improves neurological recovery and reduces complications in DCM surgery. • Less experienced surgeons particularly benefit from following CSS recommendations. • The combination of CSS recommendation and surgical expertise fosters better patient outcomes in DCM management.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".