Self‐reported compliance with last malaria treatment and occurrence of malaria during follow‐up in a Brazilian Amazon population
Bibliographic record
Abstract
The objective of this study was to describe the association between self-reported compliance with last malaria treatment (CMT) and occurrence of malaria during follow-up, controlling for current risk factors. We conducted a prospective open cohort study in Leonislândia, a rural area of Peixoto de Azevedo City, in the Amazon region of Mato Grosso, Brazil. A total of 414 individuals were interviewed at baseline regarding CMT and followed-up for either 8 or 4 months to assess malaria incidence. The associations between CMT and occurrence of malaria were examined through multiple linear regression (when the outcome was malaria episode frequency) or Cox regression (when the outcome was time to malaria onset). Poor CMT (prior to baseline) was identified as an important predictor of the occurrence of subsequent malaria episodes during follow-up among individuals with an indication of being less immune - those whose first malaria episode was relatively recent or those who had an increased number of malaria episodes during the last 2 years. Moreover, surprisingly, it seems that for individuals who are probably more immune (individuals who had experienced their first malaria episode more than 4.5 years previously or those with few or no malaria episodes during the last 2 years), CMT was found to be a poor predictor of increased risk of subsequent malaria. These findings provide compelling evidence for the need to further study CMT and its effect on malaria outcomes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".