203 ASSESSMENT OF NEUROPATHIC PAIN PATIENTS IN A FAMILY PRACTICE SETTING USING DN4 QUESTIONNAIRE AND QUANTITATIVE SENSORY TESTING
Bibliographic record
Abstract
Background. Quantitative sensory testing (QST) involves the use of computer-assisted technology to measure thermal (cold/hot) detection/pain thresholds. Recent research has validated the use of such systems including the provision of normative data [R. Rolke. Pain, 2006] and reliability in patient groups, i.e. diabetic peripheral neuropathy [S.J. Bird. Muscle Nerve, 2006]. Most testing has largely been confined to pharmaceutical trials [V. Bril, Muscle Nerve, 1998]. Applications of QST are now emerging in its use outside of such studies. For example, cold hyperalgesia predicted a higher level of pain and disability in motor vehicle accident patients [M. Sterling. Pain, 2005]. Method. Twelve consecutive primary care patients with peripheral and central neuropathic pain conditions (diabetic neuropathy, post-herpetic neuralgia, failed surgical syndromes, and multiple sclerosis) who score 4 or more on the DN4 scale [D. Bouhassira. Pain, 2005] were examined with simple tools (pin, brush, cold non-vibrating tuning fork, and 10 gm monofilament). Patients underwent EMG-nerve conduction studies and QST (Medoc). Several were also initiated on evidence-based Health Canada approved medications: Pregabalin for painful diabetic neuropathy and post-herpetic neuralgia [N.B. Finnerup. Pain, 2005] and Sativex for Multiple Sclerosis associated central pain [D.J. Rog. Neurology, 2005]. Results. The clinical exam findings of brush allodynia and/or cold hypoesthesia correlated well with abnormal QST (9 patients). QST also appeared more sensitive than traditional EMG (sural-radial amplitude ratio) [B.U.H. Overbeek. Muscle Nerve, 2005] in detecting early neuropathy in diabetics with painful feet. Conclusion. These preliminary observations suggest that QST may be helpful in the primary care setting, particularly in the early diagnosis and management of neuropathic pain.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.174 | 0.129 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".