In-Vivo Animal Trial of a Fiber-Optic Pressure Sensor Probe with Distributed Sensing Points for the Diagnosis of Lumbar Spinal Stenosis
Bibliographic record
Abstract
This paper reports a fiber-optic pressure sensor probe with multiple measuring points for simultaneous time and location resolved pressure sensing.Two different measurement principles were combined to a hybrid sensor which allows sensing before, along and after stenosis at the same time.At the tip a Fabry-Pérot-Interferometer is formed by a reflective multilayer membrane of a pressure sensor chip and an optical fiber.The sealed cavity provides absolute pressure sensing.20 millimeter below the fiber tip, six Fiber Bragg Gratings are integrated.The first Fiber Bragg Grating is reinforced for additional temperature sensing.The five following Fiber Bragg Gratings act as sensing array for continuous pressure monitoring.The sensing components are coated with a silicone mantle of 1.5 mm diameter.The complete signal evaluation is done with a Fiber Bragg Grating interrogation device.The in-vivo trial with a pig allowed the determination of pressure relations and pulse wave velocity in the lumbar spinal canal.A stenosis was simulated by inflating a balloon inserted in parallel to the probe.For the first time, lumbar spinal stenosis was diagnosed by the measurement technique of the presented probe.This is a new diagnostic approach to verify the indication for surgery.First, mechanical structure aspects and the evaluation technique of our probe is described.Then, experimental results of the in-vivo trial are presented.From these results the possibility of implementing a new diagnostic method for the frequent lumbar spinal stenosis disease with claudication using to our sensing system could be derived.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".