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Record W2166404936 · doi:10.1128/jcm.02457-10

Diagnostic Performance of Filter Paper Lesion Impression PCR for Secondarily Infected Ulcers and Nonulcerative Lesions Caused by Cutaneous Leishmaniasis

2010· article· en· W2166404936 on OpenAlexaff
Andrea K. Boggild, Ana Pilar Ramos, Braulio M. Valencia, Nicolás Veland, Flor Calderon, Jorge Arévalo, Donald E. Low, Alejandro Llanos‐Cuentas

Bibliographic record

VenueJournal of Clinical Microbiology · 2010
Typearticle
Languageen
FieldMedicine
TopicResearch on Leishmaniasis Studies
Canadian institutionsUniversity of TorontoPublic Health Agency of CanadaToronto General Hospital
Fundersnot available
KeywordsLesionCutaneous leishmaniasisLeishmaniasisMedicinePathology

Abstract

fetched live from OpenAlex

We compared traditional cutaneous leishmaniasis diagnostic methods to filter paper lesion impression (FPLI) PCR for secondarily infected ulcers and nonulcerative lesions. The sensitivity and specificity of FPLI PCR for secondarily infected lesions (n = 8) were 100%. In primarily nonulcerative lesions (n = 15), the sensitivity of FPLI PCR was inferior to that of pooled-invasive-specimen PCR (72.7% versus 100%) (P = 0.10). FPLI PCR is sensitive, specific, and unlike invasive procedures, can be used in secondarily infected ulcers. Invasive specimen collection is superior in nonulcerative lesions.

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)0.003
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.039
GPT teacher head0.365
Teacher spread0.326 · 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 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

Citations26
Published2010
Admission routes1
Has abstractyes

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