Can a new design of pneumatic compression device reduce variations in delivered therapy for the mechanical prophylaxis of thromboembolic disease after total hip arthroplasty?
Bibliographic record
Abstract
BACKGROUND: Compression devices have been shown to prevent thromboembolic disease. However, the pressures generated may not be the same as the ones recommended by the manufacturer. The purpose of this study is to investigate a new sequential compression device with feedback to maintain optimal therapy, and to determine whether therapy is improved with this new device. PATIENTS AND METHOD: A series of 50 patients undergoing elective total hip arthroplasty at a major tertiary-care hospital with a special interest in joint replacement were enrolled prospectively. In addition to pharmacological prophylaxis for thromboembolic disease, all patients received compression from a modified device. Maximum pressures generated and the rate of pressure rise in each of the 3 compartments within the device sleeves were measured and the results compared with data from historical controls. RESULTS: We considered therapy to be ideal when in a particular compression cycle all chambers of both right and left sleeves reach within 10% of their target pressures at within 10% of their target pressure rise rates. The average patient received this ideal therapy 88% of the time that the new trial sequential compression device was operating. This represents a dramatic improvement over previous devices. CONCLUSIONS: The new device allows dramatically improved pressures within the device because of a feedback loop that allows dynamic control of each chamber's pressure. Improved consistency of delivery should make it easier to accurately assess the true benefits of mechanical prophylaxis with a sequential compression device.
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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 | 0.003 |
| 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) | 0.002 | 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".