IMPLANTABLE MICRO-MACHINED CAPACITIVE SENSOR TO MONITOR COMPARTMENT PRESSURES IN LOWER LIMB
Bibliographic record
Abstract
Significance Acute compartment syndrome (ACS) occurs after muscle injury and is characterised by increased pressure in the muscle compartment that can result in devastating complications if not diagnosed and treated appropriately. ACS is currently confirmed by repeated needle sticks to measure the compartment pressure using a hand-held compartment pressure monitor. This approach is often not reproducible and is not appropriate for continuous monitoring. To address the shortcomings of currently available technology we are developing an implantable micro-device that will measure compartment pressure directly and continuously over the 24 hours critical period following injury using a radio frequency identification (RFID) platform integrated with a MEMS capacitive pressure sensor. Methods The prototype implantable device measuring 3mmx3mm consists of a capacitive pressure sensor, a sensor readout circuitry, an antenna and a radio frequency reader. A prototype sensor was packaged in Silicone gel (MED-6640, Nusil Technology LLC) for ex vivo and in vivo testing in three compartment models. First, it was tested ex vivo in an airtight vessel using a blood pressure monitor to pump air and increase the pressure inside the vessel. Second, it was implanted in a muscle compartment of a fresh porcine hind limb and an infusion pump with normal saline was used to raise the tissue pressure. Third, it was implanted in the posterior thigh muscle of a rat where the pressure was increased by applying a tourniquet around the thigh. The readings were compared with those from a hand-held Stryker Intra-compartmental Pressure Monitor System used in the trauma room. Results The sensor reading from the radio frequency reader software interface in all three models showed good linearity against the pressure applied to the compartment. Conclusion The successful completion of this project will lead to the development of an implantable miniaturised wireless pressure sensor microsystem capable of measuring tissue compartment pressures in the critical period after traumatic injury and reduce the incidence of serious complications such as severe pain, paralysis, sensory deficits, muscle necrosis and permanent disability.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| 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".