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Record W2945772136 · doi:10.1093/cid/ciz148

Reply to Awandu et al

2019· letter· en· W2945772136 on OpenAlexaff
Lemu Golassa, James Cheaveau, Abu Naser Mohon, Dylan R. Pillai

Bibliographic record

VenueClinical Infectious Diseases · 2019
Typeletter
Languageen
FieldMedicine
TopicMalaria Research and Control
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicine

Abstract

fetched live from OpenAlex

To the Editor—We thank Awandu and colleagues for their insightful comments on our recent article describing ultrasensitive diagnostic tests for detection of asymptomatic malaria in a highly endemic region of Gambella, Ethiopia [1]. The higher prevalence noted in Gambella based on ultrasensitive methods is most likely due to the improved limit of detection afforded by targeting highly abundant RNA. To test this hypothesis, we evaluated a subset (n = 48) of the P. falciparum-positive samples from the Gambella study using traditional nested polymerase chain reaction (PCR) [2]. Only 60.4% (29/48) was detected positive by nested PCR (Table 1). Therefore, using standard molecular methods, the prevalence is far lower and more in line with the meta-analysis quoted [3]. We summarize the various molecular methods and their limits of detection (LOD) in Table 2. Careful consideration has to be made when combining prevalence studies using different molecular methods due to the widely varying LOD. Nested Polymerase Chain Reaction (PCR) Results of Samples Positive for Plasmodium falciparum by Quantitative Reverse Transcriptase PCR (n = 48) From the Gambella Study Abbreviation: PCR, polymerase chain reaction. Nested Polymerase Chain Reaction (PCR) Results of Samples Positive for Plasmodium falciparum by Quantitative Reverse Transcriptase PCR (n = 48) From the Gambella Study Abbreviation: PCR, polymerase chain reaction. Variation in the Limit of Detections of the Different Molecular Tools for Diagnosing Plasmodium falciparum (modified from [12]) Abbreviations: LOD, limit of detection; NASBA, nucleic acid sequence-based amplification; PCR, polymerase chain reaction; qPCR, quantitative polymerase chain reaction; qRT-PCR, quantitative reverse transcriptase polymerase chain reaction; rRNA, ribosomal RNA; RT, reverse transcriptase; TARE-2, telomere-associated repetitive element 2; US-LAMP, ultrasensitive loop mediated amplification. Variation in the Limit of Detections of the Different Molecular Tools for Diagnosing Plasmodium falciparum (modified from [12]) Abbreviations: LOD, limit of detection; NASBA, nucleic acid sequence-based amplification; PCR, polymerase chain reaction; qPCR, quantitative polymerase chain reaction; qRT-PCR, quantitative reverse transcriptase polymerase chain reaction; rRNA, ribosomal RNA; RT, reverse transcriptase; TARE-2, telomere-associated repetitive element 2; US-LAMP, ultrasensitive loop mediated amplification. The role that very low level infections (<100 parasites per mL) detected only by ultrasensitive methods play in onward transmission remains unclear and necessitates further study. Despite no linear correlation between parasite count and gametocyte count, low parasitemia infections tend to produce a smaller number of infective gametocytes [4]. However, transmission depends not only on the number of gametocytes produced but also on the efficiency of the vectors, vector diversity, and the number of bites [5–7]. Furthermore, parasitemia can fluctuate in the asymptomatic individual and longitudinal studies suggest that gametocyte counts can also oscillate, implying these very low level infections may ultimately lead to onward transmission [8]. More studies like that of Hoffman et al on ultrasensitive rapid diagnostic tests and gametocyte carriage in asymptomatic individuals are needed, especially when coupled to membrane feeding assays [9]. Some studies have attempted to model the reservoir and the probability of onward transmission, but these models require validation [10]. Ultimately, public health programs need to evaluate the role of ultrasensitive diagnostics in reactive case detection, for example, in order to determine the value of such tools in accelerating elimination [11]. Potential conflicts of interest. All authors: No reported conflicts of interest. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.040
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.146
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.040
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0090.005
Scholarly communication0.0090.005
Open science0.0030.004
Research integrity0.1460.070
Insufficient payload (model declined to judge)0.0130.010

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 head0.046
GPT teacher head0.407
Teacher spread0.361 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

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Citations3
Published2019
Admission routes1
Has abstractno

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