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Record W4285587819 · doi:10.7189/jogh.12.03027

Unlocking the global health potential of dried blood spot cards

2022· article· en· W4285587819 on OpenAlexaff
Brianne Bota, Victoria Ward, Monica Lamoureux, Emeril Santander, Robin Ducharme, Steven Hawken, Beth K. Potter, Raphael Atito, Bryan Nyamanda, Stephen Munga, Nancy A. Otieno, Sowmitra Chakraborty, Samir K. Saha, Jeffrey S. A. Stringer, Humphrey Mwape, Joan T. Price, Hilda Mujuru, Gwendoline Chimhini, Thulani Magwali, Pranesh Chakraborty, Gary L. Darmstadt, Kumanan Wilson

Bibliographic record

VenueJournal of Global Health · 2022
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical studies and practices
Canadian institutionsBruyèreUniversity of OttawaNewborn Screening OntarioChildren's Hospital of Eastern OntarioOttawa Hospital
Fundersnot available
KeywordsDried blood spotMedicineEnvironmental healthBiology

Abstract

fetched live from OpenAlex

A key challenge to providing care for newborn infants in low- and middle-income countries (LMICs) is the lack of timely diagnostic testing due to weak local infrastructures such as laboratory capacity and imaging technologies. To address this challenge, low-cost diagnostic testing that does not require extensive laboratory processing or costly storage procedures has been prioritised. […]

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)0.011
metaresearch head score (Gemma)0.020
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: Empirical · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0050.005
Open science0.0010.004
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0180.004

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 head0.043
GPT teacher head0.443
Teacher spread0.400 · 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
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

Citations7
Published2022
Admission routes1
Has abstractyes

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