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Record W2741637821 · doi:10.1016/s2468-2667(17)30120-2

Mobile phone and handheld microscopes for public health applications

2017· letter· en· W2741637821 on OpenAlexaff
Isaac I. Bogoch, Johan Lundin, Nathan C. Lo, Jason R. Andrews

Bibliographic record

VenueThe Lancet Public Health · 2017
Typeletter
Languageen
FieldImmunology and Microbiology
TopicParasites and Host Interactions
Canadian institutionsToronto General Hospital
Fundersnot available
KeywordsMobile phonePublic healthScopusMobile devicePhonemHealthHealth careMedicineInternet privacyComputer scienceMEDLINEWorld Wide WebPolitical scienceTelecommunicationsPathology

Abstract

fetched live from OpenAlex

Microscopes are crucial for diagnosis of both communicable and non-communicable diseases, and are an essential tool in public health laboratories; unfortunately, these are not available to more than a billion people who live in resource-constrained settings.1 Over the past decade, there have been several important advances in the development of low-cost, portable microscopy tools designed for use in resource-limited environments. Handheld microscopes, particularly those interfacing with a mobile phone, hold promise for facilitating the provision of equitable health care to those living in underserviced setings.

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)0.001
metaresearch head score (Gemma)0.005
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: Commentary · Consensus signal: none
Teacher disagreement score0.158
Threshold uncertainty score0.529

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.006
Science and technology studies0.0010.000
Scholarly communication0.0030.005
Open science0.0020.003
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.1580.070

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 head0.115
GPT teacher head0.389
Teacher spread0.274 · 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".

Quick stats

Citations17
Published2017
Admission routes1
Has abstractyes

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