Diagnostic uncertainty and medical geography: what are we mapping?
Bibliographic record
Abstract
The administration of the Canadian health care system requires the collection of large quantities of health data that some health researchers have used to map the spatial distribution of disease. The authors discuss the difficulty of separating genuine geographic variations in health and disease from geographic differences in how diseases are diagnosed, and how these diagnoses are represented in an administrative data system. Although there have been attempts to deal with this problem at the international scale, little research has considered the issue at intranational or intraprovincial scales. There are several strategies available that can help separate spatial patterns of disease from nonmedical confounders, though they remain largely untested in medical geography. Future research should consider the scale of the problem and the effectiveness of existing approaches in mitigating the effects of geographic diagnostic inconsistency on the representation of health statistics in space.
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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 | 0.059 | 0.355 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.019 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.015 | 0.027 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| 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".