A Systematic Review of the Utility of the Hoffmann Sign for the Diagnosis of Degenerative Cervical Myelopathy
Bibliographic record
Abstract
STUDY DESIGN: Systematic review. OBJECTIVE: To determine the validity of the Hoffmann sign for the detection of degenerative cervical myelopathy (DCM) for patients presenting with cervical complaints. SUMMARY OF BACKGROUND DATA: While physical examination maneuvers are often used to diagnose DCM, no previous review has synthesized diagnostic accuracy data. METHODS: Medline, Embase, and HealthStar were searched for articles from January 1, 1947 to March 1, 2017 using the following terms: Spinal Cord Diseases, Spinal Cord Compression, Cervical Vertebrae, Signs and Symptoms, Physical Examination, Epidemiologic studies, Epidemiologic Research Design, Predictive Value of Tests, and Myelopathy. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS) checklist was applied to determine the level of evidence. Articles included were published in English or French language, rated as QUADAS level 3 or higher with a minimum 10 patients presenting with cervical complaints having undergone the Hoffman sign. Excluded studies recruited patients with a nondegenerative type of cervical myelopathy, and/or no evaluation with magnetic resonance imaging. RESULTS: A total of 589 articles were selected for review. Following the application of inclusion and exclusion criteria, 45 articles were analyzed using the QUADAS checklist. Only of three articles were of QUADAS quality 3 or higher. Analysis of combined data from 2/3 studies indicated that the Hoffman sign has a positive likelihood ratio of 2.2 (95% CI 1.5-3.3) and a negative likelihood ratio of 0.63 (95% CI 0.5-0.8). CONCLUSION: A positive Hoffman alone is unlikely to lead to more than a small change in estimated probability of DCM as compared with the gold standard test (magnetic resonance imaging). Variability in results across individual studies may result from differences in study design. There are insufficient data to support use of the Hoffman sign alone to confirm or refute a diagnosis of DCM. LEVEL OF EVIDENCE: 1.
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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 | 0.019 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.009 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".