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Record W4394542687 · doi:10.6084/m9.figshare.14303339

Application of the Egipss model in the regional hospitals of Mato Grosso do Sul

2021· dataset· en· W4394542687 on OpenAlexaboutno aff
Beatriz Figueiredo Dobashi, Alethele de Oliveira Santos, Crhistinne Cavalheiro Maymone Gonçalves, Eugenio Oliveira Martins de Barros, Fernando Cupertino De Barros

Bibliographic record

VenueFigshare · 2021
Typedataset
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsnot available
Fundersnot available
KeywordsGeographyEnvironmental scienceForestry

Abstract

fetched live from OpenAlex

In 2007, the Pact for Health developed by the Secretary of Health of the State of Mato Grosso do Sul included the reorganization of hospital care according to territorial distribution and structure of regional hospitals in cities head of micro regions of the State. An evaluation was done to gather data to support central decisions for the improvement of those units and also to furnish a set of measures of outcome that could guide and be incorporated into the management routine of those regional hospitals. An agreement with the National Council of Health Secretaries allowed the participation of experts of the Montreal University, in Canada, in the construction of the evaluation here described, together with the personnel of the Health Secretary of the State of Mato Grosso do Sul, the regional hospitals and personnel of other states of Brazil.

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)0.002
metaresearch head score (Gemma)0.010
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: Dataset · Consensus signal: Dataset
Teacher disagreement score0.148
Threshold uncertainty score0.294

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0160.008

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 head0.029
GPT teacher head0.302
Teacher spread0.272 · 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
GenreDataset

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

Citations0
Published2021
Admission routes1
Has abstractyes

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