Optimization of diagnostic procedures in primary health services to detect asymptomatic malaria
Bibliographic record
Abstract
Background.The biggest challenge today is the accuracy of diagnostic tools to detect asymptomatic malaria.Up to the present, a microscopic examination procedure is only performed on patients with fever; thus, finding asymptomatic malaria is quite impossible.A serial microscopic examination (SME) procedure on patients who are at risk of malaria would make it possible to detect asymptomatic malaria.Objectives.This study was done to find cases of asymptomatic malaria through the optimization of malaria diagnostic procedures at the primary health care facilities in the Batubara District, North Sumatera Province of Indonesia.Material and methods.SME was conducted for three consecutive days once a microscopic examination provided a negative result.A diagnosis of malaria is confirmed by optimization of routine microscopic examination (ORME).SME is then carried out on the 2 nd day (first SME), the 8 th day (second SME) and the 15 th day (third SME).An examination was declared negative once Plasmodium sp. is not found up to 500 high power field.Results.SME of 1,597 patients who had negative results on the first microscopic examination revealed that 95 had submicroscopic malaria (5.9%).This study found asymptomatic malaria in 20.3% of the study subjects ( 188 persons) at first microscopic examination, 3.7% (34 persons) at first SME and 3% (28 persons) at second SME.Conclusions.ORME and SME performed on people at risk of malaria provide the possibility to detect asymptomatic malaria.
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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 | 0.010 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".