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Record W2298380952 · doi:10.1155/2008/289657

Delayed Tuberculosis Treatment in Urban and Suburban Ontario

2008· article· en· W2298380952 on OpenAlexaffabout
Andrea S. Gershon, Wendy Wobeser, Jack V. Tu

Bibliographic record

VenueCanadian Respiratory Journal · 2008
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsHealth Sciences CentreUniversity of TorontoKingston General HospitalQueen's UniversitySunnybrook Health Science CentreInstitute for Clinical Evaluative Sciences
Fundersnot available
KeywordsMedicineInterquartile rangeTuberculosisDiseasePublic healthPediatricsPopulationSurgeryInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: Delay in the treatment of patients with tuberculosis (TB) increases the risk of poor clinical outcomes--including death and transmission of disease--and may be reducible. OBJECTIVE: To estimate delays in TB treatment in a Canadian, multicultural population and to examine factors associated with longer time to treatment. METHODS: Adult cases of active TB from January 1998 to December 2001 from the Ontario Reportable Disease Information System were included. Time to treatment was defined as the number of days between symptom onset and treatment. RESULTS: Data from 1753 TB patients (76% of eligible patients) were analyzed. Median time to treatment was 62 days (interquartile range 31 to 114 days). Time periods longer than the median time to treatment were independently associated with middle-aged patients (OR 1.54, 95% CI 1.21 to 1.98), foreign-born patients who had lived in Canada for more than 10 years (OR 1.47, 95% CI 1.02 to 2.12), patients with nonpulmonary disease (OR 1.57, 95% CI 1.28 to 1.92) and patients managed within certain health districts. CONCLUSION: A time to TB treatment of two months or more is common in Ontario, and associated with several factors. Future studies are needed to build on these findings to decrease delay and improve individual and public health outcomes.

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 score0.425
Threshold uncertainty score0.806

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.047
GPT teacher head0.290
Teacher spread0.244 · 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

Citations8
Published2008
Admission routes2
Has abstractyes

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