Area under the Curve: Analysis of Approach-Related Recovery Time in 165 Operative Cervical Spondylotic Myelopathy Patients with a 2-Year Follow-Up
Bibliographic record
Abstract
Introduction Much debate about postoperative outcomes regarding surgical approaches for cervical spondylotic myelopathy (CSM) exists in the literature with no clear evidence of superiority. We propose a novel method for assessing health-related quality of life (HRQOL) outcomes by taking into account each patient's baseline at postoperative time points and analyzing the “area under the curve” (AUC), a proxy for suffering time. Patients and Methods Post hoc analysis of a prospective, multicenter database of patients with CSM. A total of 165 patients met the following inclusion criteria: symptomatic CSM, age older than 18 years, and 2-year follow-up with modified Japanese Orthopaedic Association (mJOA) and neck disability index (NDI). The anterior approach group (AAG) ( n = 110) and posterior approach group (PAG) ( n = 55) were compared at baseline, 1 year, and 2 years for each HRQOL. This comparison was repeated with normalization, using the patient's baseline as the anchor, followed by an integration and comparison of AUC. Results and Conclusion: For the first time, AUC analysis was applied to evaluating patients with CSM. Nonnormalized HRQOLs demonstrated the AAG started higher and met better standards at all times points compared with the PAG. Normalized mJOA demonstrated the PAG actually did better at 2 years, whereas NDI suggested that the AAG did better, although this was not significant. AUC analysis further supported the superiority of the PAG, with statistical significance at 1 and 2 years' time points, suggesting that patients who undergo the posterior approach may suffer less in the first 2 years of their postoperative course.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".