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Record W3023753643 · doi:10.1016/s2589-7500(20)30081-9

Can technology help improve diarrhoea management?

2020· letter· en· W3023753643 on OpenAlexaff
Zulfiqar A Bhutta

Bibliographic record

VenueThe Lancet Digital Health · 2020
Typeletter
Languageen
FieldMedicine
TopicViral gastroenteritis research and epidemiology
Canadian institutionsSickKids Foundation
Fundersnot available
KeywordsScopusMedicineGuidelineFamily medicinePediatricsMEDLINEPolitical science

Abstract

fetched live from OpenAlex

Diarrhoeal diseases continue to account for a substantial proportion of deaths and morbidity in young children globally.1 Despite vast knowledge around prevention and management, the burden remains high and, in particular, coverage rates of oral rehydration therapy remain stagnant at around 40% globally.2 Despite adequate guidelines from WHO on appropriate management of dehydrated cases of diarrhoea in hospitals, there is concern that quality of care for childhood diarrhoea varies considerably and might contribute to persisting diarrhoea morbidity and mortality globally.

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)0.021
metaresearch head score (Gemma)0.120
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: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.105
Threshold uncertainty score0.350

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.120
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.005
Science and technology studies0.0010.004
Scholarly communication0.0100.017
Open science0.0030.006
Research integrity0.0070.006
Insufficient payload (model declined to judge)0.1050.031

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 head0.040
GPT teacher head0.331
Teacher spread0.291 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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