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HUBUNGAN KOMPRES BAWANG MERAH (ALLIN ESENSIAL OIL) DENGAN PENURUNAN DEMAM PADA BALITA DI KAMPUNG HASIK JAYA SORONG SELATAN

2022· article· en· W4283797443 on OpenAlexaff
Fatmawati Putri, Retno Wulan

Bibliographic record

VenueCoping Community of Publishing in Nursing · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicShallot Cultivation and Analysis
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsEssential oilToxicologyMedicineTraditional medicineBiologyFood science

Abstract

fetched live from OpenAlex

Shallots can be used to compress, this is because onions contain organic sulfur compounds, namely allycysteine ??sulfoxide (aliin) which functions to destroy blood clots. Other ingredients of shallots that can lower body temperature are phlorogusin, cycloaliin, methylaliin, and kaemferol. The purpose of this study was the effect of compressing shallots (allin essential oil) on reducing fever in children under five in Hasik Jaya Village, Moswaren District, South Sorong Regency. This type of research is a quasi-experimental research with pre-test and post-test with control group design. The number of samples is 31 respondents. The results showed most of the age of toddlers with fever in Hasik Jaya Village, Moswaren District, South Sorong Regency were 13-24 months as many as 20 toddlers (64,5%), female as many as 18 toddlers (58,1%). Some of the toddlers' temperature before being given red onion compresses (allin essential oil) was 37,80C for 10 toddlers (32,3%). Some of the toddlers' temperature after being given an onion compress (allin essential oil) was 37,50C for 12 toddlers (38,7%). There is a relationship between shallot compresses (allin essential oil) and fever reduction in children under five in Hasik Jaya Village, Moswaren District, South Sorong Regency with p value of 0,000.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0120.001

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 head0.279
Teacher spread0.236 · 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 designObservational
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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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