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Pooling Cervical Swabs and Testing by Ligase Chain Reaction Are Accurate and Cost-Saving Strategies for Diagnosis of <i>Chlamydia trachomatis</i>

2000· article· en· W2134074932 on OpenAlexaff
J. Kapala, Debby Copes, Arlene Sproston, Jay S. Patel, D. Jang, Astrid Petrich, James B. Mahony, Kathleen Biers, Max Chernesky

Bibliographic record

VenueJournal of Clinical Microbiology · 2000
Typearticle
Languageen
FieldImmunology and Microbiology
TopicReproductive tract infections research
Canadian institutionsSt. Joseph’s Healthcare HamiltonMcMaster UniversitySt. Joseph's Hospital
Fundersnot available
KeywordsLigase chain reactionChlamydia trachomatisPoolingNucleic Acid Amplification TestsFalse positive paradoxMultiplexPopulationPolymerase chain reactionBiologyMedicineVirologyBioinformaticsMultiplex polymerase chain reactionComputer scienceGeneticsEnvironmental health

Abstract

fetched live from OpenAlex

Specimen pooling to achieve efficiency when testing urine specimens for Chlamydia trachomatis nucleic acids has been suggested. We pooled endocervical swabs from 1,288 women and also tested individual swabs by ligase chain reaction (LCR). Out of 53 positive specimens, pools of 4 or 8 specimens missed two positives, providing 96.2% accuracy compared to individual test results. Dilution and positive-control spiking experiments showed that negative specimens with inhibitors of LCR in the pool reduced the signal. Conversely, two extra positives, detected only through pooling, were negative by individual testing but became positive after storage, suggesting that fresh positive specimens with labile inhibitors may be positive in a pool because of dilution of inhibitors. For this population of women with a 4% prevalence of C. trachomatis infection, substantial savings in cost of reagents (55 to 63%) and technologist time (50 to 63%) made pooling strategies a desirable alternative to individual testing.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.561
Threshold uncertainty score0.623

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.070
GPT teacher head0.381
Teacher spread0.311 · 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 designBench or experimental
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

Citations36
Published2000
Admission routes1
Has abstractyes

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