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Record W261907566 · doi:10.5430/jha.v6n3p46

A comparison of clinical trial and model-based cost estimates in glaucoma – The case of repeat laser trabeculoplasty In Ontario

2017· article· en· W261907566 on OpenAlexafffundvenueabout
Ahmad Omar Akhtar, Janet Martin, Gregory S. Zaric, Francie Si, Cindy Hutnik, William Hodge

Bibliographic record

VenueJournal of Hospital Administration · 2017
Typearticle
Languageen
FieldMedicine
TopicGlaucoma and retinal disorders
Canadian institutionsWestern University
FundersCanadian Institutes of Health Research
KeywordsIndirect costsChristian ministryActivity-based costingMedical costsMedicineGlaucomaPsychological interventionClinical trialCost–benefit analysisHealth careEconomicsNursingOphthalmology

Abstract

fetched live from OpenAlex

Economic evaluations of glaucoma interventions require accurate costs in addition to effectiveness data. However, the impact of different costing methods on cost estimates has not been investigated. Direct cost estimates alongside clinical trials may be labour-intensive and expensive, modelled cost using literature sources and institutional experience may be an alternative. We investigated modeled and directly collected costs of a trial comparing argon- and selective-laser trabeculoplasty (ALT and SLT) among glaucoma patients at St. Joseph’s Health Care in London, ON between 2013 and 2014, also comparing ministry and societal perspectives and cost drivers. Model and trial cost estimates differed minimally for the ministry perspective (8% and 4% for ALT and SLT) despite differences in modeled and observed parameter values and treatment pathways. Labour accounted for 90% of total cost. Costs were similar for the societal perspective although there was sensitivity to assumptions regarding patient time loss. Indirect costs were at least as large as direct medical costs. Modeled costs were an acceptable substitute for directly measured costs in this scenario.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.967

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.055
GPT teacher head0.397
Teacher spread0.342 · 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

Citations1
Published2017
Admission routes4
Has abstractyes

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