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Оценка эффективности и безопасности применения доксофиллина при постковидном респираторном синдроме у пациентов с продолжающимся и долгим ковидом

2022· article· ru· W4307410304 on OpenAlexaff
Тамаз Маглакелидзе, Иванэ Чхаидзе, Нана Дзидзигури, Салия Гоча, Клайв Пейдж, Арзу Бегдамирова

Bibliographic record

VenueInterConf · 2022
Typearticle
Languageru
FieldMedicine
TopicRespiratory and Cough-Related Research
Canadian institutionsNorthern Lipids (Canada)
Fundersnot available
KeywordsMedicine

Abstract

fetched live from OpenAlex

В данном исследовании изучалась эффективность и безопасность доксофиллина у пациентов с продолжающимся и долгим ковидом при развитии постковидного респираторного синдрома. В параллельном, нерандомизированном, перспективном, многоцентровом исследовании участвовали 157 пациентов(Тбилиси, Кутаиси, Зугдиди, Баку), длительность исследования составила 9 месяцев. Для оценки использовалась шкала тяжести кашля(Cough Evaluation Test), модифицированная шкала одышки MRC (Medical Research Council), спирометрические данные (FEV1 и FEV1/FVC (индекс Тиффно)). Было установлено, что доксофиллин достоверно снижает интенсивность кашля, уменьшает одышку и улучшает функцию легких.

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)0.003
metaresearch head score (Gemma)0.008
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: none
Teacher disagreement score0.039
Threshold uncertainty score0.131

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0020.003
Scholarly communication0.0070.004
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0390.015

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 head0.035
GPT teacher head0.322
Teacher spread0.287 · 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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