Bibliographic record
Abstract
OBJECTIVE: , a lesser-known tick-borne illness increasing in prevalence in Canada. SOURCES OF INFORMATION: with a focus on epidemiology, diagnosis, and management. Levels of evidence for treatment recommendations ranged from II to III, with most available data coming from case reports, case series, retrospective studies, and systematic reviews. MAIN MESSAGE: Tick-borne illnesses are becoming more prevalent in Canada due to the effects of climate change. Anaplasmosis is a lesser-known tickborne illness transmitted by the same vectors as Lyme disease. Patients with anaplasmosis commonly present with a nonspecific febrile illness, often without a distinctive rash. Leukopenia, thrombocytopenia, and mild transaminitis are commonly seen in bloodwork results. Family physicians in Canada should familiarize themselves with this relatively new entity and include it in their differential diagnosis when evaluating patients with fever of unknown origin. In such cases, polymerase chain reaction testing for anaplasmosis, alongside Lyme disease serology, should be considered. Patients with either disease respond well to treatment with doxycycline. CONCLUSION: With the expanding range of tick populations in Canada, the risk of anaplasmosis, along with other tick-borne illnesses, is becoming more prevalent. Increased awareness among family physicians is critical for timely diagnosis. Prompt recognition and treatment can reduce the risk of complications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".