Predictors of hospitalization of tuberculosis patients in Montreal, Canada: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Hospitalization is the most costly health system component of tuberculosis (TB) control programs. Our objectives were to identify how frequently patients are hospitalized, and the factors associated with hospitalizations and length-of-stay (LOS) of TB patients in a large Canadian city. METHODS: We extracted data from the Montreal TB Resource database, a retrospective cohort of all active TB cases reported to the Montreal Public Health Department between January 1996 and May 2007. Data included patient demographics, clinical characteristics, and dates of treatment and hospitalization. Predictors of hospitalization and LOS were estimated using logistic regression and Cox proportional hazards regression, respectively. RESULTS: There were 1852 active TB patients. Of these, 51% were hospitalized initially during the period of diagnosis and/or treatment initiation (median LOS 17.5 days), and 9.0% hospitalized later during treatment (median LOS 13 days). In adjusted models, patients were more likely to be hospitalized initially if they were children, had co-morbidities, smear-positive symptomatic pulmonary TB, cavitary or miliary TB, and multi- or poly-TB drug resistance. Factors predictive of longer initial LOS included having HIV, renal disease, symptomatic pulmonary smear-positive TB, multi- or poly-TB drug resistance, and being in a teaching hospital. CONCLUSIONS: We found a high hospitalization rate during diagnosis and treatment of patients with TB. Diagnostic delay due to low index of suspicion may result in patients presenting with more severe disease at the time of diagnosis. Earlier identification and treatment, through interventions to increase TB awareness and more targeted prevention programs, might reduce costly TB-related hospital use.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".