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Record W4292680240 · doi:10.3201/eid2809.220092

Costs of Tuberculosis at 3 Treatment Centers, Canada, 2010–2016

2022· article· en· W4292680240 on OpenAlexfundaboutno aff
Jonathon R. Campbell, Placide Nsengiyumva, Leslie Chiang, Frances Jamieson, Hadeel Khadawardi, Henry K.-H. Mah, Olivia Oxlade, Hayden Rasberry, Elizabeth Rea, Kamila Romanowski, Natasha F. Sabur, Beate Sander, Aashna Uppal, James C. Johnston, Kevin Schwartzman, Sarah K. Brode

Bibliographic record

VenueEmerging infectious diseases · 2022
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchMcGill University Health CentreMcGill University
KeywordsIsoniazidMedicineInterquartile rangeTuberculosisMulti-drug-resistant tuberculosisInternal medicineRetrospective cohort studyExtensively drug-resistant tuberculosisMycobacterium tuberculosisPediatricsPathology

Abstract

fetched live from OpenAlex

We estimated costs of managing different forms of tuberculosis (TB) across Canada by conducting a retrospective chart review and cost assessment of patients treated for TB infection, drug-susceptible TB (DS TB), isoniazid-resistant TB, or multidrug-resistant TB (MDR TB) at 3 treatment centers. We included 90 patients each with TB infection and DS TB, 71 with isoniazid-resistant TB, and 62 with MDR TB. Median per-patient costs for TB infection (in 2020 Canadian dollars) were $804 (interquartile range [IQR] $587-$1,205), for DS TB $12,148 (IQR $4,388-$24,842), for isoniazid-resistant TB $19,319 (IQR $7,117-$41,318), and for MDR TB $119,014 (IQR $80,642-$164,015). Compared with costs for managing DS TB, costs were 11.1 (95% CI 9.1-14.3) times lower for TB infection, 1.7 (95% CI 1.3-2.1) times higher for isoniazid-resistant TB, and 8.1 (95% CI 6.1-10.6) times higher for MDR TB. Broadened TB infection treatment could avert high costs associated with managing TB disease.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.454
Threshold uncertainty score0.998

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)0.0030.000

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 head0.012
GPT teacher head0.280
Teacher spread0.267 · 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.

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

Citations20
Published2022
Admission routes2
Has abstractyes

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