Comparison of Kato–Katz, PCR and coproantigen for the diagnosis of <i>Taenia solium</i> taeniasis
Bibliographic record
Abstract
Abstract Four methods were compared for the diagnosis of human taeniasis caused by Taenia solium. Fecal samples from persons living in a T. solium endemic region of Madagascar were examined for taeniid eggs by the Kato–Katz method. Subsequently, samples positive (n = 16) and negative (n = 200) for T. solium eggs were examined by (i) amplification of the fragment of small subunit of the mitochondrial ribosomal RNA (rrnS) gene using conventional polymerase chain reaction (PCR) and (ii) a nested PCR of a fragment of the T. solium Tso31 gene. Additionally, 12 egg-positive and all egg-negative samples were tested for coproantigen detection. A further 9 egg-positive fecal samples were examined using both PCRs. Of the 12 egg-positive samples tested by PCRs and coproantigen methods, 9 (75%) were positive by rrnS PCR, 3 (25%) using Tso31-nested PCR and 9 (75%) by coproantigen testing. None of the 200 egg-negative fecal samples was positive in either rrnS or Tso31-nested PCR. Twenty of the 25 egg-positive samples (80%) were positive in rrnS PCR, and DNA sequencing of PCR amplicons was obtained from 18 samples, all confirmed to be T. solium. Twelve of the 25 egg-positive samples (48%) were positive in the Tso31-nested PCR, all of which were also positive by rrnS PCR. It is suggested that species-specific diagnosis of T. solium taeniasis may be achieved by either coprological examination to detect eggs or coproantigen testing, followed by rrnS PCR and DNA sequencing to confirm the tapeworm species in egg-positive or coproantigen-positive samples.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".