PainVision apparatus is effective for assessing low back pain after fusion surgery
Bibliographic record
Abstract
Purpose.In the current study, we aimed to evaluate the efficacy of PainVision, a tool for assessing the perception of pain in a quantitative manner, for assessing postsurgical low back pain.Methods.We assessed 42 patients with low back pain after fusion surgery.All patients underwent fusion surgery with posterior instrumented fixation.The numeric rating scale (NRS) score, McGill Pain Questionnaire (MPQ) score, and degree of pain using PainVision PS-2100 were measured twice at 4-week intervals in each patient.For PainVision measurements an electrode was patched on the forearm surface of the patients, and the degree of pain was calculated automatically.The degree of pain was evaluated using both the current producing pain comparable with low back pain and the current at perception threshold.Correlations between NRS and MPQ scores and the degree of pain were determined statistically.Results.There was a statistical correlation between the NRS and MPQ scores at each time point (r s ＞0.56, P ＝0.001) .The degree of pain evaluated by PainVision also showed statistical correlation with NRS and MPQ scores at each time point (r s ＞044, P ＜0.02) .Change in the degree of pain evaluated by PainVision over 4 weeks showed a statistical correlation with changes in NRS and in MPQ scores (r s ＞0.4,P ＜0.01) .Conclusion.PainVision is useful for assessing postsurgical low back pain.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".