Divergent associations between incident hypertension and deprivation based on different sources of case identification
Bibliographic record
Abstract
INTRODUCTION: Studies suggest that hypertension is more prevalent in the most deprived. Our objective was to examine the association between incident hypertension and deprivation in Quebec based on different modes of case identification, using two administrative databases. METHODS: We identified new incident cases of hypertension in 2006/2007 in the population aged 20 years plus. Socio-economic status was determined using a material and social deprivation index. Negative binomial regression analyses were carried out to examine the association between incident hypertension and deprivation, adjusting for several covariates. RESULTS: We found a positive and statistically significant association between material deprivation and incident hypertension in women, irrespective of the identifying database. Using the hospitalization database, the incidence of hypertension increased for both sexes as deprivation increased, except for social deprivation in women. However, whether using the physician billing data base or the validated definition of hypertension obtained by combining data from the two databases, the incidence of hypertension decreased overall as deprivation increased. CONCLUSIONS: Associations between hypertension and deprivation differ based on the database used: they are generally positively associated with the hospitalization database and inversely with the standard definition and the physician billing database, which suggests a consultation bias in favour of the most socio-economically advantaged.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".