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Record W3009093235 · doi:10.1002/mds3.10068

Current view and prospect: Implantable pressure sensors for health and surgical care

2020· article· en· W3009093235 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMedical Devices & Sensors · 2020
Typearticle
Languageen
FieldEngineering
TopicNon-Invasive Vital Sign Monitoring
Canadian institutionsSickKids FoundationHospital for Sick ChildrenMcGill University
Fundersnot available
KeywordseHealthmHealthTerminologyContinuous monitoringHealth careRemote patient monitoringMedicineClinical decision support systemComputer scienceRisk analysis (engineering)Intensive care medicineDecision support systemEngineeringData miningOperations managementNursing

Abstract

fetched live from OpenAlex

Abstract Health monitoring and screening have entered a period of rapid change. Popular terminology refers to this as mobile health (mHealth), which is a direct evolution of eHealth, but is really data‐driven technology—sensors oriented for health care. Medical decision support through this technology is the first step towards more personalized and preventative medicine. Pressure is one of the easiest and most interesting physiological parameters to assess whether organs or biological systems are healthy in the body. Pressure recordings are commonly used for clinical diagnosis and monitoring; however, the invasiveness of current technologies and associated risks of infection limit the windows in which data can be gathered. This review discusses the importance of pressure in the body and how monitoring is performed. It also describes newer and commercially available sensors, as well as how they can be improved to become minimally invasive, fully wireless pressure sensors for continuous monitoring.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.932
Threshold uncertainty score0.858

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.274
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it