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Record W4380301843 · doi:10.1115/dmd2023-6655

CAPAPP: SMARTPHONE-BASED CAPILLARY REFILL INDEX ASSESSMENT IN HEALTHY CHILDREN

2023· article· en· W4380301843 on OpenAlexaff
Jonathan Strutt, Girish Narayanswamy, Chunjong Park, Devesh Sarda, Sixuan Wu, Matthew Thompson, Lauren Harvey, Rachel Hedstrom, Amy J. Kodet, Shwetak Patel, Alex Mariakakis

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldHealth Professions
TopicPressure Ulcer Prevention and Management
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCapillary refillMedicineVital signsSmartphone applicationCapillary actionPopulationBlood pressureComputer scienceAnesthesiaInternal medicineMaterials science

Abstract

fetched live from OpenAlex

Abstract Capillary refill time is the time it takes for blanched skin tissue under pressure to return to its normal state when that pressure is released. The test is commonly performed on the fingertip by clinicians to assess for signs of septic, traumatic, or hypovolemic shock. Current methods of capillary refill time measurement are typically subjective, coarse-grained, and clinician dependent. A more standardized and objective measurement of capillary refill time has been shown to improve the diagnosis of pediatric dehydration but has typically required specialized equipment. We have developed a digital version of the capillary refill test utilizing only a smartphone to increase access to capillary refill assessment. Our aim is to determine the accuracy and precision of this smartphone-based measure in a population of healthy pediatric subjects.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.046
GPT teacher head0.430
Teacher spread0.384 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations2
Published2023
Admission routes1
Has abstractyes

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