Grading of MRI and STarT Back Screening Tool (SBST) in the diagnosis of severity of lumbar central canal stenosis: a sensitivity analysis
Bibliographic record
Abstract
Purpose: This study aimed to correlation between the grading stenosis and the STarT Back Screening Tool (SBST) in patients diagnosed with lumbar central canal stenosis (LCCS). Methods: In a prospective clinical study, a total of 269 patients with LCCS were asked to respond to the questionnaire at their first visits. Grading of LCCS on MRI was determined and also the severities of the disease were observed based on SBST as the gold standard. Finally grading on MRI and calcification of the SBST were determined, and sensitivity analysis carried out to evaluate severity of LCCS on grading of MRI using the SBST. Results: The mean age of patients was 58.6 (SD= 10.9) years; and 56.5% were female. According to patients’ imaging they have been diagnosed as grade 1 (n = 86), grade 2 (n = 107) and grade 3 (n = 76). The sensitivity, specificity and accuracy of the estimated grading of LCCS on MRI for low, medium, and high risk groups were found to be desirable: 97.6%, 66.7%, 96.5% for low risk; 93.1%, 83.3%, 92.5% for medium risk, and 97.2%, 66.7%, 94.7% for high risk, respectively. Conclusion: Our findings indicate that grading of LCCS on MRI correlate with SBST and suggest that it is a reliable measure for screening LCCS patients.
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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 | 0.020 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".