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Record W2107268577 · doi:10.12927/hcpol.2013.23605

Looking for Lithotripsy: Accessibility and Portability of Canadian Healthcare

2013· article· en· W2107268577 on OpenAlexaffvenueabout
Katrina Piggott, Chaim M. Bell

Bibliographic record

VenueHealthcare policy · 2013
Typearticle
Languageen
FieldMedicine
TopicKidney Stones and Urolithiasis Treatments
Canadian institutionsMount Sinai HospitalMcMaster University
Fundersnot available
KeywordsHealth careSoftware portabilitySociologyPolitical scienceEngineering ethicsLibrary sciencePublic relationsEngineeringLaw

Abstract

fetched live from OpenAlex

BACKGROUND: Extracorporeal shock wave lithotripsy (ESWL) is a definitive, ambulatory and non-invasive modality for treating kidney stones. ESWL is not available in all urban centres and many Canadians must either travel, sometimes out of province, or wait to have this procedure performed. We sought to evaluate the variability in access to ESWL treatment. METHOD: We compiled a comprehensive list of ESWL centres in Canada and contacted all centres in 2011 to assess their wait times, out-of-province patient fees, and roles and responsibilities of the referring physician. RESULTS: We contacted all 23 ESWL facilities across Canada (100% response rate). Wait times for elective ESWL procedures ranged from one day to over one year, with a mean of 8.4 weeks (SD, 16.76 weeks). No centres refused out-of-province patients, although five discouraged travel to their centre owing to their prolonged wait times. No facilities charged extra fees for out-of-province patients. Ten (43%) facilities required a secondary consultation by a urolo-gist at the centre before booking. Twelve (52%) of the centres indicated the waiting time could be shortened if the referring physician were to advocate on the patient's behalf. Contact was repeated one year later in 2012 with five centres, and the results were similar. INTERPRETATION: There is marked variation in wait times across Canada for ESWL but there are few barriers to care. Patients' waits may be shortened by physician advocacy.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.792
Threshold uncertainty score0.920

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.043
GPT teacher head0.367
Teacher spread0.323 · 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

Citations8
Published2013
Admission routes3
Has abstractyes

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