The Delay in Diagnosis of Tuberculosis in the Monteregie region of Quebec, Canada
Bibliographic record
Abstract
INTRODUCTION: Despite being more prevalent in developing countries, tuberculosis (TB) remains an important health problem in Canada. Long diagnosis delays of respiratory tuberculosis are associated with adverse consequences for the patient but also for the community. From a public health perspective, identification of factors associated with long delays of diagnosis could help reduce these delays. OBJECTIVES: 1)To describe diagnosis delays of respiratory tuberculosis in Monteregie 2)To identify the characteristics of patients and factors associated with longer diagnosis delays 3)To identify consequences of these delays. METHODS: The study is descriptive and transversal. Data were obtained from notifiable diseases files of the Public Health Department of the Health and Social Services Agency of Monteregie. The diagnosis delay was calculated using the first symptomatic date and the date of diagnosis. For continued variable analyses, Student t tests and an ANOVA test were done. For categorical variables, Pearson's chi squared test and a Mann-Whitney test were done. RESULTS: The average delay of diagnosis for the 115 cases studied was 92.2 days (CI 80.6-103.8). Weight loss and/or non specific general malaise were associated with a longer diagnosis delay. No association was found between the diagnosis delay and possible consequences of longer delays. DISCUSSION AND CONCLUSION: Most patients had a diagnosis delay longer than two months. A larger study that would divide the total diagnosis delay into a patient delay and a suspicion delay (health care system delay) could permit a better identification of factors that favour long delays.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".