Tuberculosis in Southwestern Ontario Emergency Departments: A Missed Opportunity?
Bibliographic record
Abstract
Study/Objective: The primary objective is to determine the clinical presentation of emergency department patients with Tuberculosis (TB) in southwestern Ontario, and to evaluate their pre-diagnosis emergency department utilization.Patterns and clinical findings will be used to develop a center-specific TB educational resource for ED physicians, to aid in the recognition and diagnosis of high risk patients which could be used at other large Canadian, urban tertiary care hospitals.Broadly, this study aims to increase awareness of TB in local EDs.Background: The Middlesex-London Health Unit (MLHU) reports on average 10 cases of active tuberculosis (TB) per year, with 99 cases between January 2005 and December 2015.Most patients with TB heavily utilize the emergency department (ED) prior to diagnosis.Patients with TB seeking care in the ED are often unrecognized as having TB, as risk factors and symptoms are frequently missed.Delays in diagnosis of TB worsen morbidity/mortality and increases disease transmission.The emergency department may present an opportunity for earlier diagnosis and intervention.To date, no studies have been undertaken to examine TB diagnosis and burden of care in Ontario EDs.Methods: A hospital-based retrospective review of adult and paediatric patients (n = 99) identified by Middlesex-London Health Unit as having active TB between January 1st 2005 and December 31st 2015 will be performed.Health records will be reviewed 1 year prior to and 6 months after the formal TB diagnosis to determine the clinical presentation of ED patients with TB.Results: This is a proposed study.Conclusion: This is a proposed study.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".