Mapping systemic lupus erythematosus and psoriatic arthritis in greater Toronto area using geographic information systems
Bibliographic record
Abstract
Mapping Systemic Lupus Erythematosus and Psoriatic Arthritis in Greater Toronto Area Using Geographic Information Systems; by Mustafa H. Al-Maini, MD FRCPC. A thesis submitted in conformity with the requirements for the degree of Master's in Health Sciences Graduate Department of Institute of Medical Sciences, University of Toronto 2008. Systemic lupus erythematosus (SLE) and psoriatic arthritis (PsA) are immune diseases that are influenced genetically and environmentally. We used geographic information systems (GIS), to map SLE and PsA in Greater Toronto Area (GTA). Using the cumulative number of observed cases of SLE or PSA diagnosed between January 1st 1965 to June 30th 2007 and excluding patients younger than 10 years and older than 69 years, and dividing by the general population age-sex match group; we calculated age-sex-GTA region observed incidence rates. These rates were then used to compute expected incidence rates for each census tract of the general population in the GTA and maps were produced. This is the first study to use GIS to map SLE and PsA in a defined geographic area. We developed a methodology to compute expected age-sex incidence maps of SLE and PsA in a defined geographic area.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".