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Record W2913078162 · doi:10.1101/537126

More accuracy estimation of the worm burden in the ascariasis of children in Kinshasa

2019· preprint· en· W2913078162 on OpenAlexaff
Guyguy Kabundi Tshima, Paul Madishala Mulumba

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2019
Typepreprint
Languageen
FieldImmunology and Microbiology
TopicParasites and Host Interactions
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsAscarisAscariasisAscaris lumbricoidesAnthelminticHelminthsConfidence intervalAnimal scienceBiologyVeterinary medicineToxicologyMedicineInternal medicineImmunology

Abstract

fetched live from OpenAlex

ABSTRACT The present study aims to give a better estimate of the worm burden (ascariasis) to address accurately the impact of intestinal parasitosis on the children growth in Africa. The study was conducted on 20 subjects aged 10 months to 10 years (Mean ± SD: 5.6 ± 2.3 years). They were treated with 10 mg/kg of Pyrantel pamoate. The next day, the stools were collected, washed and filtered to harvest all adult ascaris. In total, 141 ascaris (71 males and 70 females) were extracted for 879.9 g of stool. The geometric mean of eggs counted was 29 by 2 mg of stool. The daily eggs laying per female was estimated to 202,500 eggs/days (CI95%: 128,800 – 276,200). Statistical analysis shows that the parasitic worm burden was proportional both to the number eggs counted per unit of stool volume, and to age of infested subject. A regression model based on these two parameters, with a coefficient of determination equal to 59 %, was retained. Thus, for an old subject respectively of 1, 5 and 10 years, at which 1 egg of ascaris in approximately 2 mg of a preparation would lodge a respective parasitic mass of 1, 3 and 9 g. The results are in the form of confidence interval. For example, for a 5 years old subject with an average of 10 eggs (CI95% = 5.6 - 14.4) after reading of 2 separated preparations coming from the same specimen, the estimated parasitic load is laying between 7 and 11 g.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.523
Threshold uncertainty score0.735

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.258
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2019
Admission routes1
Has abstractyes

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