Fentanyl-TTS bei Arthrose bedingten Schmerzen
Bibliographic record
Abstract
AIM: Efficacy and tolerability of analgesic treatment with fentanyl-TTS (Durogesic) for pain caused by arthrosis was investigated. METHOD: Treatment with fentanyl-TTS was started for the first time in patients with severe pain caused by coxarthrosis and/or gonarthrosis and was prospectively documented for 30 days. Pain was assessed by the patient with an 11-step numerical analogue scale and by the physician with 5 questions (5-step scale) adapted from the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). RESULTS: A total of 243 patients (166 women, 77 men) with a mean age of 67 years (range 28 - 94) was treated. In the patient's rating, pain at rest as well as in motion decreased significantly from 6.4 +/- 2.1 to 2.9 +/- 2.0 and from 8.1 +/- 1.5 to 4.2 +/- 21, respectively (p < 0.001 for both comparisons). The sum score for the 5 questions with a detailed pain assessment by the physician decreased from 18.8 +/- 3.2 to 11.2 +/- 42 (p < 0.001). A significant pain reduction was also observed for each of the 5 single questions. At the end of observation the proportions of patients with no pain or impairment under different conditions were 5 % (climbing stairs), 14 % (walking), 26 % (standing), 33 % (lying/sitting), and 44 % (sleeping). The most common adverse events were nausea, vomiting, obstipation, and dizziness. Treatment with fentanyl-TTS was usually well-tolerated. CONCLUSION: Severe pain caused by arthrosis can be well treated with fentanyl-TTS with a favourable safety profile.
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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".