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Record W2101134105 · doi:10.12927/hcpol.2008.20265

How Busy Are Private MRI Centres in Canada?

2008· article· fr· W2101134105 on OpenAlexafffundvenueabout
Eduard Bercovici, Chaim M. Bell

Bibliographic record

VenueHealthcare policy · 2008
Typearticle
Languagefr
FieldHealth Professions
TopicHealthcare Operations and Scheduling Optimization
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsBiostatisticsHealth careSociologyHealth policyClinical epidemiologyEpidemiologyPolitical scienceLibrary scienceMedicineMedical educationEngineering ethicsEngineeringLawPathologyComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: Long waits for publicly funded magnetic resonance imaging (MRI) services have spurred the opening of private MRI centres in Canada. Little is known about the number and utilization of these facilities. METHODS: The authors surveyed all 17 private and 69 of 73 public English-speaking MRI centres in Canada in 2006, using hours of operation and waits for an elective MRI as surrogate measures of procedure volume and facility capacity. RESULTS: Public MRIs had more hours of operation on weekdays (14.7 vs. 9.7, p<0.001) and weekends (11.8 vs. 8.2, p<0.001). Waits were longer in public vs. private MRI centres (13.6 vs. 0.5 weeks, p<0.001). CONCLUSIONS: Private MRIs provided fewer hours of operation but shorter wait times compared to public centres. This finding suggests that private centres have unused capacity and relatively small procedure volumes, and provide a minority of studies.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.700
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.072
GPT teacher head0.370
Teacher spread0.298 · 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 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

Citations8
Published2008
Admission routes4
Has abstractyes

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