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Record W2395606052 · doi:10.1503/cjs.004215

Financial costs and patients’ perceptions of medical tourism in bariatric surgery

2016· article· en· W2395606052 on OpenAlexaffvenueabout
David H. Kim, Caroline E. Sheppard, Christopher J. de Gara, Shahzeer Karmali, Daniel W. Birch

Bibliographic record

VenueCanadian Journal of Surgery · 2016
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Healthcare and Medical Tourism
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineMedical tourismTourismMedical costsMedical careSurgeryHealth careFinanceFamily medicineEconomic growth

Abstract

fetched live from OpenAlex

SUMMARY: Many Canadians pursue surgical treatment for severe obesity outside of their province or country - so-called "medical tourism." We have managed many complications related to this evolving phenomenon. The costs associated with this care seem substantial but have not been previously quantified. We surveyed Alberta general surgeons and postoperative medical tourists to estimate costs of treating complications related to medical tourism in bariatric surgery and to understand patients' motivations for pursuing medical tourism. Our analysis suggests more than $560 000 was spent treating 59 bariatric medical tourists by 25 surgeons between 2012 and 2013. Responses from medical tourists suggest that they believe their surgeries were successful despite some having postoperative complications and lacking support from medical or surgical teams. We believe that the financial cost of treating complications related to medical tourism in Alberta is substantial and impacts existing limited resources.

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)0.006
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 score0.229
Threshold uncertainty score0.455

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.048
GPT teacher head0.346
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 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

Citations28
Published2016
Admission routes3
Has abstractyes

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