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Record W4387030242 · doi:10.31983/jlm.v5i2.9646

Gambaran Nilai Index Trombosit pada Pasien Tuberulosis Paru yang Mengonsumsi Obat Anti Tuberkulosis

2023· article· en· W4387030242 on OpenAlexaff
S.Y. Didik Widiyanto, Nurul Qomariyah, Eko Naning Sofyanita

Bibliographic record

VenueJaringan Laboratorium Medis · 2023
Typearticle
Languageen
FieldMedicine
TopicInflammatory Biomarkers in Disease Prognosis
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsMedicinePlateletMycobacterium tuberculosisInternal medicineTuberculosisGastroenterologyPathology

Abstract

fetched live from OpenAlex

Tuberculosis (TB) is a disease caused by the bacterium Mycobacterium tuberculosis which mostly attacks the lungs. Based on several studies, it is stated that tuberculosis sufferers who take OAT for healing experience side effects in the form of lowering the platelet index due to reactions from drugs that lyse platelets. This research is a descriptive study with a cross sectional design. Research was conducted on 30 respondents who consumed OAT. The results of this study obtained results of more than 50% on one of the platelet indices, namely the PWD value, which was obtained with low results with a total of 19 patients (63.3%), while for other indices such as platelet counts there were 21 patients (70%) , MPV in 28 patients (93%) and PCT in 23 patients (77%) had normal results. The conclusion of this study is that platelet index examinations in TB patients tend to be low in MPV and PDW, while they show high results in PLT and PCT examinations.

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)0.001
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 score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.015
GPT teacher head0.272
Teacher spread0.257 · 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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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