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Record W2026490487 · doi:10.1587/transcom.e95.b.3067

Application of a Telemedical Tool in an Isolated Island and a Disaster Area of the Great East Japan Earthquake

2012· article· en· W2026490487 on OpenAlexaff
Makoto Yoshizawa, Tomoyuki Yambe, Norihiro Sugita, Satoshi Konno, Noriyasu Homma, Futoshi Takei, Katsuhiko Yokota, Yoshifumi Saijo, Shin-ichi Nitta

Bibliographic record

VenueIEICE Transactions on Communications · 2012
Typearticle
Languageen
FieldComputer Science
TopicContext-Aware Activity Recognition Systems
Canadian institutionsInstitute of Aging
FundersOmron Healthcare
KeywordsSoftware portabilityOperabilityComputer scienceMedical emergencyTelecommunicationsComputer securityMedicine

Abstract

fetched live from OpenAlex

The present paper has reported a case study of the “Electronic Doctor's Bag” which is a telemedical tool for home-visit medical services using the mobile communications environment in an isolated island and a disaster area hit by the tsunami. Clinical trials performed for 20 patients around a clinic in Miyako Island indicated that the communication functions of the proposed system were highly evaluated by patients as well as medical staffs. However, the system still has room for further improvement in operability, portability and mobile communication environment. The experience at the shelter in Kesennuma City suggested that mobile healthcare tools such as the proposed system will be strongly required when there are no or only paramedical staffs after leaving of emergency medical staffs.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.036
GPT teacher head0.270
Teacher spread0.235 · 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

Citations3
Published2012
Admission routes1
Has abstractyes

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