Predictors of Response in Patients With Postherpetic Neuralgia and HIV-Associated Neuropathy Treated With the 8% Capsaicin Patch (Qutenza)
Bibliographic record
Abstract
OBJECTIVES: Qutenza is a high-dose capsaicin patch used to relieve neuropathic pain from postherpetic neuralgia (PHN) and HIV-associated neuropathy (HIV-AN). In clinical studies, some patients had a dramatic response to the capsaicin patch. Our objective was to determine the baseline characteristics of patients who best benefit from capsaicin patch treatment. METHODS: We conducted a meta-analysis of 6 completed randomized and controlled Qutenza studies by pooling individual patient data. Sustained response was defined as>50% decrease in the mean pain intensity from baseline to weeks 2 to 12, and Complete Response as an average pain intensity score≤1 during weeks 2 to 12. Logistic regression was used to identify predictors of response and Complete Response, and subgroups of patients who respond best to the capsaicin patch. RESULTS: Baseline pain intensity score (BPIS)≤4 was a predictor of Sustained and Complete Response in PHN and HIV-AN patients; absence of allodynia and presence of hypoesthesia, and a McGill Pain Questionnaire (MPQ) sensory score <22 were predictors of Sustained Response in PHN patients; female sex was a predictor of Sustained and Complete Response in HIV-AN patients. Thus, characteristics associated with the highest chance of responding to the capsaicin patch were, for PHN, BPIS≤4, MPQ sensory score≤22, absence of allodynia, and presence of hypoesthesia; for HIV-AN, they were female sex and BPIS≤4. Patients with these characteristics had a statistically significantly greater chance of responding to the capsaicin patch than other patients. DISCUSSION: We identified subpopulations of PHN and HIV-AN patients likely to benefit from the capsaicin patch.
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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 | 0.003 | 0.002 |
| 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; a candidate call from one teacher head, 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".