The Goutallier Classification System
Bibliographic record
Abstract
STUDY DESIGN: Retrospective, observational study. OBJECTIVE: To determine the relationship between the Goutallier classification system (GS) and anthropometric, clinical, and radiologic features in 168 patients with lumbar spinal stenosis (LSS). BACKGROUND: There is no agreement on a classification system that is both reliable and easy to use for describing the severity of fatty degeneration in the paravertebral muscles of the lower back in patients with symptomatic LSS. This study aimed to determine the statistical relationship between the GS and anthropometric, clinical, and radiologic factors in 168 patients with LSS. MATERIALS AND METHODS: This study was conducted on 168 patients with LSS scheduled for elective decompressive surgery. A control group of 110 healthy individuals was enrolled. The study assessed paralumbar musculature fatty infiltration using GS on preoperative magnetic resonance imaging. The authors evaluated the statistical association between patient age, body mass index (BMI), preoperative Oswestry disability index (ODI) questionnaire, and cross-sectional areas (CSAs) of the dural sac and lumbar paraspinal muscles. Multivariate analysis was performed to adjust for confounding. RESULTS: This study enrolled 168 patients with symptomatic LSS (95 men, 73 women); mean±SD age: 67.81±9.38 (range: 32.78-92.34) years; BMI: 28.29±3.36 (19.95-38.10) kg/m 2 . The control group was comprised of 110 healthy patients (61 men and 49 women). Age, sex, BMI, and erector spinae (ES)-CSA were not significantly different between the two groups. The authors found a direct relationship between GS grade and age and an inverse relationship between GS grade and dural sac-, multifidus lumbaris (LM)-, ES-, and psoas muscle (PM)-CSAs. Univariate analyses showed the variables statistically related to a higher GS grade included patient age ( P <0.001), ODI ( P =0.136), dural sac-CSA ( P =0.011), LM-CSA ( P < 0.001), ES-CSA ( P <0.001), and PM-CSA ( P <0.001). Multivariate least squares analysis showed the GS grade to be influenced by patient age ( P =0.01), LM-CSA ( P =0.002), ES-CSA ( P =0.002), and PM-CSA ( P =0.003). CONCLUSIONS: GS shows great potential as a tool for evaluating fat infiltration in the paralumbar muscles. This measure does not correlate with the ODI and BMI but is related to all radiologic parameters and patient age. Further prospective studies are required to establish a link between preoperative and postoperative outcomes in the setting of paraspinal fat infiltration.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".