The Diagnostic Accuracy of Gluteal Trigger Points to Differentiate Radicular From Nonradicular Low Back Pain
Bibliographic record
Abstract
OBJECTIVES: Low back pain (LBP) is highly prevalent and costly to the society. Previous studies have shown an association between radicular LBP and trigger points (TrPs) in the superior-lateral quadrant of the gluteal area (GTrP). The objective of current study was to evaluate the diagnostic value of GTrP to predict nerve root involvement among patients with LBP. MATERIALS AND METHODS: In a prospective, diagnostic accuracy study 325 consecutive patients with LBP were recruited. At first step, patients were evaluated for the presence or absence of the GTrP. A different investigator, blinded to the GTrP findings, then performed history taking and physical examination. Subsequently, all patients underwent a lumbar spine magnetic resonance imaging and, when indicated, electrodiagnostic tests. On the basis of the clinical and ancillary tests findings, a multidisciplinary panel of experts (the "reference standard"), blinded to the GTrP evaluation, allocated patients to radicular versus nonradicular LBP groups. The agreement between the GTrP findings, as a diagnostic test and the reference standard allocation was evaluated in a 2 by 2 contingency table. RESULTS: The specificity of the GTrP test was 91.4% and its sensitivity was 74.1%. The area under the receiver operating characteristic curve was 0.827 (0.781 to 0.874). Positive likelihood ratio was 8.62 and negative likelihood ratio was 0.28. Positive and negative predictive values were 91.9% and 72.7%, respectively. DISCUSSION: As a clinical finding, TrPs in superior-lateral quadrant of gluteal area are highly specific indicators for radicular LBP. Incorporating these TrPs evaluation in routine physical examination of patients with LBP could decrease the need for more costly, time-consuming, and invasive diagnostic tests.
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".