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Record W1615787060 · doi:10.1155/2008/854984

The Costs of Colonoscopy in a Canadian Hospital using a Microcosting Approach

2008· article· en· W1615787060 on OpenAlexaffvenueabout
Nour Sharara, Viviane Adam, Ralph Crott, Alan Barkun

Bibliographic record

VenueCanadian Journal of Gastroenterology · 2008
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsMcGill University
Fundersnot available
KeywordsColonoscopyMedicinePolypectomyOverhead (engineering)Indirect costsColorectal cancerVariable costCost–benefit analysisTotal costIntensive care medicineGeneral surgeryCancerComputer scienceInternal medicineBusiness

Abstract

fetched live from OpenAlex

BACKGROUND: Colonoscopy has become accepted as one of the most effective methods of screening patients for colorectal cancer, and is used to remove the majority of colonic adenomas. OBJECTIVE: Because of the paucity of such estimates in the literature and the significant number of candidates for this procedure, the present study was performed to estimate the direct hospital costs of both diagnostic and therapeutic (polypectomy) colonoscopy. METHODS: A microcosting methodology was used to itemize the costs of colonoscopy. Variable and fixed costs were divided into labour, supplies, equipment and overhead costs. A third-party payer perspective was adopted. All costs are expressed in 2007 Canadian dollars. RESULTS: The cost of a diagnostic colonoscopy was $157 and the cost of a therapeutic colonoscopy was $199. Overhead costs represented approximately 30% of these amounts. When physician fees were added, these costs rose to $352 and $467, respectively. CONCLUSION: Because the overhead costs represent a large proportion of the total costs, allocation methods for these costs should be improved to allow for a more precise determination of the total costs of a colonoscopy. These estimates are useful when analyzing the cost-effectiveness of a strategy that uses colonoscopy when screening for colorectal cancer.

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 score0.365
Threshold uncertainty score0.453

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.017
GPT teacher head0.241
Teacher spread0.224 · 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

Citations40
Published2008
Admission routes3
Has abstractyes

Explore more

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