Estimation of uncertainty in Loa loa microfilarial load by microscopy
Bibliographic record
Abstract
Abstract For determine the uncertainty of reading and measurement, as well as the Pari intervals of microfilarial load or microfilaremia (mf) per millimeter of Loa loa performed by microscopy. It is important to consider the uncertainty in the measurement or reading of the Loa Loa microfilarial load for the administration of ivermectin. We review existing methods for calculating the uncertainty in the measurement of a particular quantity, with emphasis on the one proposed in GUM. The data used here come from research conducted by CRFilMT in Ebolowa and Mbalmayo in 2007 and 2010, respectively, and in the Okola health district in Cameroon in 2015. The data consist of several measurements or readings of Loa loa load on each sampled individual. The application of the GUM method to our data was done using a 2-level hierarchical model. We estimated the uncertainty and sources of variation in the measurements and readings of Loa loa microfilarial load, and provided 95% intervals for the true values (8,000 mf/mL and 30,000 mf/mL), of this load for each individual. For reading, the reading uncertainty is 3.84 with a Pari interval of [6, 723.15, 11, 264] of the 8,000 mf/mL microfilar charge and 7.45 with a Pari interval of [26, 819.55, 35, 152.09] of the 30,000 mf/mL microfilar charge. For the measurement, the reading uncertainty is 20.93 with a Pari interval of [7, 647.32, 8, 216.26] of the 8,000 mf/mL microfilar charge and 40.53 with a Pari interval of [26, 819.55, 35, 152.09] of the 30,000 mf/mL microfilar charge.
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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 | 0.032 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".