Student Competition (Technology Innovation) ID 1985239
Bibliographic record
Abstract
Background Deep vein thrombosis (DVT) is a blood clot that forms in the deep veins and is the third most common cardiovascular disease today. One of the causes of DVT is venous stasis. Current methods of DVT prevention include anticoagulants and mechanical prophylaxis. Anticoagulant use is contraindicated in individuals with bleeding risks and mechanical interventions are often cumbersome and uncomfortable. Objective The overall goal of this project is to investigate a novel method for DVT prevention, termed intermittent electrical stimulation (IES). The current study investigated the effects of IES on healthy typical as well as post-stroke persons. Method Of the 32 participants, 22 were healthy subjects from Edmonton, and 10 were inpatient post-stroke subjects at the Glenrose Rehabilitation Hospital, Edmonton. A two-channel stimulator was used to administer IES through electrodes placed on the posterior and anterior sides of the leg and was applied in increasing stimulation amplitudes to the gastrocnemius muscle and the resulting plantar flexion force and changes in popliteal and femoral venous velocities were recorded for each stimulation respectively using B-mode ultrasound. Results IES-induced contractions produced significant increases in venous flow compared to baseline. Small contractions induced by comfortable levels of stimulation in typical and in post-stroke persons were sufficient to increase flow in the popliteal and femoral veins. Conclusion The results indicate that IES can sufficiently increase venous flow to prevent venous stasis and is comfortable for end users. Incorporation of IES into a clinical device could provide a feasible and effective alternative for DVT prophylaxis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.922 | 0.789 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".