Temperature and pressure fiber-optic sensors applied to minimally invasive diagnostics and therapies
Bibliographic record
Abstract
We present how fiber-optic temperature or pressure sensors could be applied to minimally invasive diagnostics and therapies. For instance a miniature 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. These include intra-aortic balloon pumping (IABP) therapy and other applications requiring detection of fast and/or subtle fluid pressure variations such as for intracranial pressure monitoring or for urology diagnostics. As well, miniature temperature sensors permit minimally invasive direct temperature measurement in diagnostics or therapies requiring energy transfer to living tissues. The extremely small size of fiber-optic sensors that we have developed allows quick and precise in situ measurements exactly where the physical parameters need to be known. Furthermore, their intrinsic immunity to electromagnetic interference (EMI) allows for the safe use of EMI-generating therapeutic or diagnostic equipments without compromising the signal quality. With the trend of ambulatory health care and the increasing EMI noise found in modern hospitals, the use of multi-parameter fiber-optic sensors will improve constant patient monitoring without any concern about the effects of EMI disturbances. The advantages of miniature fiberoptic sensors will offer clinicians new monitoring tools that open the way for improved diagnostic accuracy and new therapeutic technologies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".