Reduction of Prolonged Excessive Pressure in Seated Persons With Paraplegia Using Wireless Lingual Tactile Feedback: A Randomized Controlled Trial
Bibliographic record
Abstract
Pressure ulcers (PU) are known to be a high-cost disease with a risk of severe morbidity. This paper evaluates a new clinical strategy based on an innovative medical device [Tongue Display Unit (TDU)] that implements perceptive supplementation in order to reduce prolonged excessive pressure, recognized as one of the main causes of PU. A randomized, controlled, and parallel-group trial was carried out with 12 subjects with spinal cord injuries (SCI). Subjects were assigned to the control (without TDU, [Formula: see text]) or intervention (with TDU, [Formula: see text]) group. Each subject took part in two sessions, during which the subject, seated on a pressure map sensor, watched a movie for one hour. The TDU was activated during the second session of the intervention group. Intention-to-treat analysis showed that the improvement in adequate weight shifting between the two sessions was higher in the intervention group (0.84 [0.24; 0.89]) than in the control group (0.01 [-0.01; 0.09]; [Formula: see text]) and that the ratio of prolonged excessive pressure between the two sessions was lower in the intervention group (0.74 [0.37; 1.92]) than in the control group (1.72 [1.32; 2.56]; [Formula: see text]). The pressure map sensor was evaluated as being convenient for use in daily life; however, this was not the case for the TDU. This paper shows that persons with SCI could benefit from a system based on perceptive supplementation that alerts and guides the user on how to adapt their posture in order to reduce prolonged excessive pressure, one of the main causes of PU.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.006 | 0.001 |
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".