THE CLINICAL EFFECTs AND INFLUENCE FACTORS OF TREATMENT FOR POST-HERPETIC NEURALGIA BY INJECTING DOXORUBICIN TO DORSAL ROOT GANGLION NEURONS
Bibliographic record
Abstract
Objective: To analysis the clinical effects and influence factors of treatment for postherpetic neuralgia(PHN) by injecting doxorubicin to dorsal root ganglion neurons.Methods: 65 patients randomly recruited were divided into three groups: short term( 1 years),medium term(1 ~ 3 years) and long-term( 3 years).Paravertebral injections to dorsal root ganglion were guided by imaging,the comprehensive evaluation of treatment effect was evaluated using a simplified pain scale(short-form McGill pain questio-nnaire,SF-MPQ),and then the pations were follow-up,evaluated of pain degree,duration of sleep and other symptoms,and analysed factors affecting the treatment.Results: SF-MPQ before treatment was 21.90 ± 4.98.SF-MPQ after 1 weeks of treatment(9.78 ± 1.35) and follow-up(6.95 ± 0.97) scores were significantly reduced compared with before treatment,there were significant differences(P 0.01);Sleep time(3.09 ± 0.29 h) increased.There were no significant difference among the short,medium,long term groups(9.78 ± 1.35,9.66 ± 1.25,9.57 ± 1.31 in each group).Conclusion: The interventional treatment of neuralgia after herpes zoster by injecting doxorubicin to dorsal root ganglion is a safe,effective and long lasting treatment method.The factors affecting the therapeutic effect included age,the duration of posthepetic neuralgia and the immunocompromised factor.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".