Bibliographic record
Abstract
Establishing the diagnosis of Q fever (Coxiella burnetii) is important in directing the application of therapy to prevent severe manifestations of the infection. In Alberta, Canada, the presence of high livestock density creates a significant risk of infection, but to date, there has been no comprehensive analysis of local Q fever epidemiological trends and exposure patterns. Between 1998 and 2011, there were 39 cases and an overall adjusted case rate of 0.087 per 100 000 person-years. Cases were identified most commonly during the May-June season (Figure 2). The median age at date of diagnosis was 49.0 (range: 8.7-71.5) with slightly higher percentage of cases in men (56.4%) than in women (43.6%). There was an apparent geographical clustering of cases. The majority of these cases, with exposure data (n = 31), reported contact with farms and/or livestock, predominantly cattle (6), sheep (5) and goats (5). Cases tended to occur in census divisions with higher density of sheep, goats and cattle. Our findings suggest the need for an increase in targeted messages about Q fever to those in the livestock industry, as more targeted case finding among patients with a high index of suspicion for Q fever. In addition, widespread implementation of a standard questionnaire for cases would enhance surveillance of Q fever in Alberta.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".