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Record W2619091049 · doi:10.1093/cid/cix081

Standardization of Laboratory Methods for the PERCH Study

2017· article· en· W2619091049 on OpenAlexaff
Amanda J. Driscoll, Ruth A. Karron, Susan C. Morpeth, Niranjan Bhat, Orin S. Levine, Henry C. Baggett, W. Abdullah Brooks, Daniel R. Feikin, Laura L. Hammitt, Stephen R. C. Howie, Maria Deloria Knoll, Karen L. Kotloff, Shabir A. Madhi, J. Anthony G. Scott, Donald M. Thea, Peter V. Adrian, Dilruba Ahmed, Muntasir Alam, Trevor P. Anderson, Martín Antonio, Vicky L. Baillie, Michel Dione, Hubert P. Endtz, Caroline W. Gitahi, Angela Karani, Geoffrey Kwenda, Abdoul Aziz Maiga, Jessica McClellan, Joanne Mitchell, Palesa Morailane, Daisy Mugo, John Mwaba, James Mwansa, Salim Mwarumba, Sammy Nyongesa, Sandra Panchalingam, Mohammad Mustafizur Rahman, Pongpun Sawatwong, Boubou Tamboura, Aliou Touré, Toni Whistler, Katherine L. O’Brien, David R. Murdoch

Bibliographic record

VenueClinical Infectious Diseases · 2017
Typearticle
Languageen
FieldMedicine
TopicPneumonia and Respiratory Infections
Canadian institutionsUniversity of Calgary
FundersWellcome TrustAlere
KeywordsMedicineStandardizationPneumoniaQuality assuranceEtiologyIntensive care medicineStandard operating procedurePathologyInternal medicineExternal quality assessment

Abstract

fetched live from OpenAlex

The Pneumonia Etiology Research for Child Health study was conducted across 7 diverse research sites and relied on standardized clinical and laboratory methods for the accurate and meaningful interpretation of pneumonia etiology data. Blood, respiratory specimens, and urine were collected from children aged 1-59 months hospitalized with severe or very severe pneumonia and community controls of the same age without severe pneumonia and were tested with an extensive array of laboratory diagnostic tests. A standardized testing algorithm and standard operating procedures were applied across all study sites. Site laboratories received uniform training, equipment, and reagents for core testing methods. Standardization was further assured by routine teleconferences, in-person meetings, site monitoring visits, and internal and external quality assurance testing. Targeted confirmatory testing and testing by specialized assays were done at a central reference laboratory.

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)0.091
metaresearch head score (Gemma)0.076
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: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.091
Threshold uncertainty score0.480

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0910.076
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0110.007
Science and technology studies0.0020.003
Scholarly communication0.0040.002
Open science0.0060.004
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0100.009

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 head0.108
GPT teacher head0.558
Teacher spread0.450 · 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
GenreMethods

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

Citations53
Published2017
Admission routes1
Has abstractyes

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