The CITRUS approach: validated transcranial ultrasound stimulation through steerable transducers and MR acoustic radiation force imaging
Bibliographic record
Abstract
however, research has suggested the heterogeneity of treatment effects across participants.This study aimed to identify the sociodemographic and clinical predictors of such heterogeneity in older adults with symptomatic KOA undergoing tDCS, thereby enhancing personalized treatment strategies.Specifically, we analyzed active and sham tDCS groups separately to account for placebo or sham effects.This study entailed secondary data analysis of a double-blind, randomized, sham-controlled, phase II, parallelgroup pilot clinical trial involving 120 participants with KOA pain.These participants were assigned to 15 daily telehealth-delivered sessions of either active 2-mA tDCS (n¼60) for 20 min or sham stimulation (n¼60) over 3 weeks.The primary outcome was the change in Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain subscale scores, measured from baseline to after the 15 tDCS sessions in both the active and sham groups.Predictive modeling using random forest (RF) and artificial neural network (ANN) algorithms was utilized, with model performance assessed based on R-squared values.The impact of predictive features on treatment outcomes was examined using several feature selection methods, including Lasso, BorutaSHAP, Chi2, F-regression, and Rregression.The RF and ANN models both effectively predicted treatment effects, indicating the potential of machine learning to enhance patientspecific treatment strategies.In the active group, the predominant features included age, average heat pain tolerance at the knee at baseline, baseline WOMAC functional score, and the duration of KOA.In the sham group, the major features comprised the duration of KOA, KellgreneLawrence scale score of the affected knee, baseline pain catastrophizing score, average heat pain tolerance at the knee at baseline, and baseline WOMAC functional score.Characterizing these predictive factors can inform personalized tDCS protocols, potentially improving treatment effects.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".