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Record W2940748999 · doi:10.3747/co.26.4405

Establishing Funding Rates for Colonoscopy and Gastroscopy Procedures in Ontario

2019· article· en· W2940748999 on OpenAlexaffvenueabout
Julia Monakova, J Wong, Irene Blais, Aimee Langan, Nabeela Ratansi, David Morgan, Nancy N. Baxter

Bibliographic record

VenueCurrent Oncology · 2019
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsUniversity of TorontoSt. Joseph's HospitalPublic Health OntarioSt. Joseph’s Healthcare HamiltonOntario Medical AssociationMastercard FoundationMcMaster UniversityCancer Care Ontario
Fundersnot available
KeywordsMedicineColonoscopyGeneral surgeryFamily medicineColorectal cancerInternal medicineCancer

Abstract

fetched live from OpenAlex

Introduction: This paper describes the funding rates established in Ontario to reflect best practices in hospital-based care delivery for these endoscopic procedures: colonoscopy, colonoscopy biopsy, gastroscopy, gastroscopy biopsy, and colonoscopy combined with gastroscopy. Methods: The funding rates are based on direct costs and were established using a micro-costing approach after receipt of inputs from 3 working groups and a review of the administrative data and literature, where applicable. The first group advised on nursing activities, time, and staffing ratios along the patient pathway for each of the procedures. The second group provided recommendations about the duration for each procedure, and the third group provided information about supplies and equipment, their use, and costs. Results: The resulting funding rates are $161.18 for colonoscopy and $151.08 for gastroscopy (without accompanying interventions), $16.06 for colonoscopy biopsy and $8.22 for gastroscopy biopsy (added to the respective procedures), and $207.26 for combined colonoscopy and gastroscopy. Detailed costs for each component embedded in the rates are also provided. Conclusions: The rates came into effect in April 2018. The process and outcomes described here allowed for a transparent pricing mechanism in which funding follows the patient, clinical expert consensus is the basis for practice, and providers and payers both understand the components.

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)0.013
metaresearch head score (Gemma)0.040
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.923
Threshold uncertainty score0.559

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.040
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0060.006
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.090
GPT teacher head0.415
Teacher spread0.326 · 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 designNot applicable
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
Published2019
Admission routes3
Has abstractyes

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