Miniature fiber optic pressure sensor for medical applications: an opportunity for intra-aortic balloon pumping (IABP) therapy
Bibliographic record
Abstract
In this paper we present how a miniature fiber optic pressure sensor based on micro-optical mechanical systems (MOMS) could solve most of the problems associated with fluidic pressure transduction presently used for triggering purposes in IABP therapy. The small size of the MOMS (0 550 μm) allows a positioning of the sensor directly at the tip of the intra-aortic catheter, exactly where the pressure should be monitored. With outstanding performances in terms of resolution and frequency fidelity, this absolute pressure sensor can precisely detect small pressure variations such as the dicrotic notch in the intra-aortic pressure waveform, which is used as a trigger point in IABP therapy. Such technology could probably help in the development of a less invasive therapy with reduced catheter size associated with reduction of vascular complications such as ischemia. The presented optical fiber sensor has intrinsic immunity to electromagnetic fields and noise perturbations. Furthermore, the patented white-light cross-correlation technology of the signal conditioner makes it immune to optical fiber binding and highly tolerant to optical losses. Such solution is extremely well adapted for in situ pressure monitoring in many medical applications.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".