Monitoring the Clinical Response to an Innovative Transdermal Delivery System for Ibuprofen
Bibliographic record
Abstract
We present a phase 1 study that utilizes a crossover design that provides a rapid and relatively inexpensive methodology for evaluating a new transdermal product. The treatment for osteoarthritis (OA) aims to reduce pain and improve function. An innovative magnetophoresis technology has been developed that facilitates transdermal delivery of ibuprofen. The study used measures that were taken over a relatively short time period to monitor the pharmacodynamic response to ibuprofen. Each participant received magnetophoresis-enhanced transdermal ibuprofen or placebo in randomised order, with a five-day washout period. The participants were 24 volunteers with medically diagnosed, painful knee OA. The primary outcome measures were VAS rating of pain on movement and Western Ontario and McMaster Universities (WOMAC) pain and function scores. VAS for pain on movement (p < 0.001), WOMAC pain score (p = 0.004), and WOMAC function score (p = 0.004) were all significantly improved. There was a significant reduction in movement-related pain (p < 0.05) during the first patch application and for the remainder of the study period. The number needed to treat for a 50% reduction in movement related pain was 2.2. The study showed a rapid and significant analgesic effect in response to transdermal ibuprofen. A short trial of this nature can be used for informing the parameters that are required for a major randomised controlled trial.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".