Estimation of limb occlusion pressure by adaptation of oscillometry for surgical tourniquet control
Bibliographic record
Abstract
Pneumatic tourniquets are widely used in surgery of the extremities to occlude the vessels of the limb, thereby providing a bloodless field for dissection so that the surgeon may operate more quickly and accurately. Over-pressurization of the tourniquet cuff may lead to postoperative complications such as temporary or permanent paralysis of the limb. This motivated the development of adaptive tourniquet systems which could regulate the tourniquet pressure just above the limb occlusion pressure (LOP), or the minimum tourniquet pressure required to prevent blood flow past the cuff for a given duration. Previous adaptive tourniquet systems suffered from several problems which limited their practical utility in the operating room. This thesis describes the adaptation of oscillometry, a technique widely used in the noninvasive estimation of blood pressure, to the estimation of LOP in the surgical environment for application in a clinically practical adaptive tourniquet system. Improved oscillometric LOP estimation performance was obtained through the development of a filter for increasing the signal-to-noise ratio of the oscillometric pulses during periods of limb manipulation, the development of a heuristic real-time pattern recognition algorithm for extracting oscillometric pulses from signal data corrupted by limb movements, and the development of a new method for rapidly estimating the LOP which needs only one-third of the signal data required by a widely-used oscillometric approach to produce an estimate of comparable accuracy. In addition to these contributions, a new tourniquet cuff was developed which achieves an improved fit to the limb, thereby enhancing performance and reliability over that obtained from conventional tourniquets as both an oscillometric occlusion sensor and as a limb-occluding device. An adaptive tourniquet system which integrated these improvements was developed and used in a clinical study involving four orthopaedic surgeons and 16 patients. Clinical trials of the latest system version in which circumstances permitted the use of adaptive control showed that the average limb-applied pressure was reduced by 35%, or from the conventional standard of 250 mm Hg to 162 mm Hg, in the upper limb surgeries, and by 38%, or from the conventional standard of 300 mm Hg to 187 mm Hg, in the lower limb surgeries. These significant reductions in the pressure indicate the potential effectiveness of adaptive tourniquet control and improved cuff design on reducing the risk of patient injuries from excessive tissue compression. Furthermore, unlike all previous implementations, this system is currently being evaluated on a routine basis in orthopaedic surgical procedures performed at Vancouver General Hospital.
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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 | 0.000 | 0.002 |
| 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) | 0.000 | 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".