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Record W2012711408 · doi:10.1089/tmj.2005.11.130

Acquisition and Evaluation of Radiography Images by Digital Camera

2005· article· en· W2012711408 on OpenAlexfundno aff
Stephen Cone, Laura R. Carucci, Jinxing Yu, Azhar Rafiq, Charles R. Doarn, Ronald C. Merrell

Bibliographic record

VenueTelemedicine Journal and e-Health · 2005
Typearticle
Languageen
FieldMedicine
TopicRadiology practices and education
Canadian institutionsnot available
FundersMitacsVirginia Commonwealth University
KeywordsTeleradiologyMedical diagnosisFile Transfer ProtocolComputer scienceRadiographyComputer visionArtificial intelligenceImage qualityThe InternetDigital cameraModalitiesMedical physicsMedicineTelemedicineRadiologyImage (mathematics)World Wide WebHealth care

Abstract

fetched live from OpenAlex

To determine applicability of low-cost digital imaging for different radiographic modalities used in consultations from remote areas of the Ecuadorian rainforest with limited resources, both medical and financial. Low-cost digital imaging, consisting of hand-held digital cameras, was used for image capture at a remote location. Diagnostic radiographic images were captured in Ecuador by digital camera and transmitted to a password-protected File Transfer Protocol (FTP) server at VCU Medical Center in Richmond, Virginia, using standard Internet connectivity with standard security. After capture and subsequent transfer of images via low-bandwidth Internet connections, attending radiologists in the United States compared diagnoses to those from Ecuador to evaluate quality of image transfer. Corroborative diagnoses were obtained with the digital camera images for greater than 90% of the plain film and computed tomography studies. Ultrasound (U/S) studies demonstrated only 56% corroboration. Images of radiographs captured utilizing commercially available digital cameras can provide quality sufficient for expert consultation for many plain film studies for remote, underserved areas without access to advanced modalities.

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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.860
Threshold uncertainty score0.246

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.031
GPT teacher head0.375
Teacher spread0.344 · 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.

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

Citations17
Published2005
Admission routes1
Has abstractyes

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