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Record W60801086

The digital readiness of imaging facilities in Saskatchewan.

2004· article· en· W60801086 on OpenAlexaffabout
Brent Burbridge, Cliff Bell

Bibliographic record

VenuePubMed · 2004
Typearticle
Languageen
FieldMedicine
TopicDigital Radiography and Breast Imaging
Canadian institutionsRoyal University Hospital
Fundersnot available
KeywordsMedicineDigital imagingThe InternetRadiology information systemsQuality (philosophy)Internet accessDigital imageMedical physicsMedical emergencyRadiologyWorld Wide WebComputer scienceArtificial intelligenceImage processing
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine the digital readiness of Saskatchewan's imaging facilities. METHODS: A questionnaire was mailed to all 173 imaging facilities in Saskatchewan, ranging from small private clinics to tertiary care hospitals. The 129 responses were received, tabulated and summarized. RESULTS: Of the 129 facilities that responded, only 2 had picture archiving and communication systems (PACS). Both were private, urban imaging facilities. Six facilities had digital radiology information systems, 12 had digital hospital information systems and 8 had digital patient records. Only 42 sites had Internet access in their facilities. CONCLUSION: Only a small minority of Saskatchewan imaging facilities have any digital capability whatsoever. None are prepared to make the transition to a fully digital environment at this time. The infrastructure required to send or receive high-quality digital images among imaging facilities in Saskatchewan does not exist. A strategy to address the implementation of digital imaging and PACS should be developed at a provincial level.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.400
Threshold uncertainty score0.804

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.009
GPT teacher head0.202
Teacher spread0.193 · 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
Published2004
Admission routes2
Has abstractyes

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