Pain Quality Predicts Lidocaine Analgesia among Patients with Suspected Neuropathic Pain
Bibliographic record
Abstract
OBJECTIVE: Oral sodium channel blockers have shown mixed results in randomized controlled trials despite the known importance of sodium channels in generating pain. We hypothesized that differing baseline pain qualities (e.g. "stabbing" vs "dull") might define specific subgroups responsive to intravenous (IV) lidocaine-a potent sodium channel blocker. DESIGN: A prospective cohort study of 71 patient with chronic pain suspected of being neuropathic were recruited between January 2003 and July 2007 and underwent lidocaine infusions at Stanford University Hospital in a single-blind nonrandomized fashion. Baseline sensory pain qualities were measured with the Short-Form McGill Pain Questionnaire (SF-MPQ). Pain intensity was measured with a visual analog scale (VAS). RESULTS: Factor analysis demonstrated two underlying pain quality factors among SF-MPQ sensory items: a heavy pain and a stabbing pain. Baseline heavy pain quality, but not stabbing quality predicted subsequent relief of pain intensity in response to lidocaine. In contrast, these factors did not predict divergent analgesic responses to placebo infusions. In response to each 1 mcg/mL increase in lidocaine plasma level, patients with high heavy pain quality drop their VAS 0.24 (95% CI 0.05-0.43) more points than those with low heavy pain quality (P < 0.013). CONCLUSIONS: "Heavy" pain quality may indentify patients with enhanced lidocaine responsiveness. Pain quality may identify subgroups among patients with suspected neuropathic pain responsive to IV lidocaine. Further investigation is warranted to validate and extend these findings.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".