MétaCan
Menu
Back to cohort
Record W3206941524 · doi:10.3389/fdgth.2021.730951

Developing Medical Technologies for Low-Resource Settings: Lessons From a Wireless Wearable Vital Signs Monitor–neoGuard

2021· article· en· W3206941524 on OpenAlexfundno aff
Assumpta Nantume, Sona Shah, Teresa Cauvel, Matthew Tomback, Ryan D. Kilpatrick, Bushra Afzal, Noah Kiwanuka

Bibliographic record

VenueFrontiers in Digital Health · 2021
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsnot available
FundersGrand Challenges Canada
KeywordsVital signsWearable computerCommercializationHealth technologyWirelessResource (disambiguation)Health careWearable technologyComputer scienceKey (lock)BusinessRisk analysis (engineering)MedicineComputer securityTelecommunicationsMarketingPolitical scienceEmbedded system

Abstract

fetched live from OpenAlex

The neoGuard™ technology is a wireless wearable vital signs monitor attached to a patient's forehead to continuously measure oxygen saturation, pulse rate, respiratory rate and temperature. Developed with feedback from more than 400 health workers, primarily in East Africa, the product has been designed to meet the unique constraints of low-resource settings. This perspective piece by the innovators of neoGuard™ and some of their key partners examines the complicated journey of taking a medical technology from concept through clinical validation and finally to market. By shedding light on some of the most critical steps and common challenges encountered along the pathway to commercialization, the authors hope that their experiences will provide some valuable insights to other aspiring innovators in this space.

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.

How this classification was reachedexpand

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.797
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.033
GPT teacher head0.389
Teacher spread0.357 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueFrontiers in Digital HealthSame topicMobile Health and mHealth ApplicationsFrench-language works237,207