Bibliographic record
Abstract
Objective To determine the prevalence of selected chronic diseases among newly arrived refugee patients and to explore associations with key demographic factors. Design Retrospective chart review. Setting Primary care clinic for refugee patients in Toronto, Ont. Participants A total of 1063 refugee patients rostered at the clinic from December 2011 to June 2014. Main outcome measures Demographic information (age, sex, and region of birth) and prevalence of abnormal Papanicolaou test results, anemia, elevated blood pressure (BP), and markers of prediabetes or diabetes (elevated random glucose, fasting glucose, or hemoglobin A1c levels). Results More than half of our patients were female (56%) and the median age was 29 years. Patients originated from 87 different countries of birth. Top source countries were Hungary (210 patients), North Korea (119 patients), and Nigeria (93 patients). Most patients were refugee claimants (92%), as opposed to government-assisted refugees (5%). Overall, 11 % of female
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.826 | 0.707 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".