Análise espacial da taxa de detecção de casos suspeitos de síndrome congênita pelo vírus Zika, Maranhão, 2015 a 2018
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: To identify spatial patterns in cases of changes in growth and development related to Zika virus infection and other infectious etiologies (denominated Zika virus congenital syndrome in this study) reported in Maranhão from 2015 to 2018 and their relation with socioeconomic and demographic variables. METHODS: Ecological study of notified Zika virus congenital syndrome cases in the 217 cities of Maranhão, Brasil. Spatial autocorrelation was calculated using GeoDa 1.14 software and the local and global (I) Moran's index in univariate and bivariate analyses on Zika virus congenital syndrome incidence rate with Municipal Human Development Index (MHDI), population density, Gini coefficient and the cities' time of administrative political emancipation. Local Moran's Index was calculated to identify clusters with significant spatial autocorrelation. RESULTS: Spatial autocorrelation was checked in univariate analysis of the incidence rate of Zika virus congenital syndrome (I=0,494; p=0,001) and positive correlation in bivariate analysis of the incidence rate with Municipal Human Development Index (I=0,252; p=0,001), population density (I=0,338; p=0,001) and the cities' time of administrative political emancipation (I=0,134; p=0,001). The correlation between incidence rate with Gini coefficient was not significant (I= -0,033; p=0,131). Five high-incidence clusters were found in distinct areas of the state. CONCLUSIONS: Cities with higher MHDI, higher population density and more years of administrative political emancipation had more cases of Zika virus congenital syndrome notified.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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 it