Increasing Awareness of Low Incidence Disease as Immigration Rates Increase: A Recent Case of Pott’s Disease in Atlantic Canada
Bibliographic record
Abstract
As Canada experiences and prepares for notable increases in immigration, health care professionals need to be aware of emerging cases of low incidence diseases, particularly when found in higher rates in the foreign-born population. Therefore, we report the case of a 20-year-old male immigrant presenting to a Canadian emergency department with a one year history of worsening back pain. In the emergency department, thoracic radiographs showed pathologic fractures at T10 and T11 with destructive changes from T8 to T12. Further, a computed tomography scan identified a large paravertebral abscess from T6 to L1, with osseous destruction and spinal stenosis. Magnetic resonance imaging showed bony deformity, epidural, pre- and paravertebral, and bilateral psoas abscesses, and a right-sided pleural effusion. Diagnosis was confirmed with direct molecular testing and treatment was initiated in a timely and efficacious manner. This case report highlights the need for health care providers to have a high index of suspicion and consideration of atypical presentations of low incidence diseases, particularly within the burgeoning immigrant population, to ensure quality health care services are delivered.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| 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".