Cutaneous larva migrans in Canadian travellers returning from the Caribbean: A 10-year surveillance analysis from CanTravNet
Bibliographic record
Abstract
Background: Cutaneous larva migrans (CLM) is one of the most common dermatoses affecting travellers to the tropics. Objective: To describe demographic and travel correlates of travellers returning to Canada from the Caribbean with CLM over a 10-year pre-pandemic period. Methods: Demographic and travel-related data on ill travellers encountered either during or after completion of their travel/migration and seen in any of eight CanTravNet sites from January 1, 2009, to December 31, 2018, with a final diagnosis of CLM were extracted and analyzed. During this time, access to first-line therapy, ivermectin, was available via Health Canada's Special Access Programme. Results: Of 17,644 travellers presenting to CanTravNet over the enrolment period, 328 (1.9%) returned from the Caribbean with CLM. The median age of travellers with CLM was 34 years (interquartile range: 25-50 years), with females accounting for 58% of cases. Ninety-five percent (n=313) travelled for tourism. Jamaica was the most common source country, with 216 cases (67%), followed by Barbados (n=27, 8%) and the Dominican Republic (n=23, 7%). Cases in 2018 were imported predominantly from Jamaica (n=58, 73%) and the Dominican Republic (n=12, 15%). Age, sex and purpose of travel were similar across years. The percentage of all imported cases of CLM that originated from the Caribbean increased from 9% in 2016 to 24.5% in 2018. Conclusion: Proportions and absolute numbers of CLM in travellers returning to Canada from the Caribbean are increasing. Improved awareness of this common dermatosis among physicians and travellers, as well as improved access to effective therapies, will reduce associated morbidity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".