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Record W4408346472 · doi:10.47353/jsocmed.v2i7.68

The Association Between ASPECTS Score and Dysphagia in Acute Ischemic Stroke Patients

2023· article· en· W4408346472 on OpenAlexaboutno aff
Diko Hamonangan Saragih, Cut Aria Arina, Iskandar Nasution

Bibliographic record

VenueJournal of Society Medicine · 2023
Typearticle
Languageen
FieldHealth Professions
TopicDysphagia Assessment and Management
Canadian institutionsnot available
Fundersnot available
KeywordsDysphagiaMedicineIschemic strokeAssociation (psychology)Stroke (engine)Internal medicineCardiologySurgeryIschemiaPsychology

Abstract

fetched live from OpenAlex

Introduction: The Alberta Stroke Program Early CT Score (ASPECTS) is a scoring system assessed from CT scans to investigate the involvement of brain regions experiencing early ischemic changes. Lower scores are known to be associated with worse conditions and the occurrence of complications such as dysphagia. This study aimed to assess the relationship between ASPECTS scores and the occurrence of dysphagia in patients with acute ischemic stroke. Method: This cross-sectional analytical study was conducted at RSUP H. Adam Malik Medan from November 2022 to January 2023. ASPECTS scores were assessed based on CT scan results, while dysphagia was evaluated using the GUSS score. The Mann-Whitney U test was performed to assess the relationship between the two variables. Results: : A total of 34 subjects were included in this study. Among them, 11 (32.4%) subjects experienced dysphagia. The median ASPECTS score was 8 (range 3-9). The Mann-Whitney U test showed a significant association between ASPECTS scores and the occurrence of dysphagia (p<0.001). Conclusion: There is a significant relationship between ASPECTS scores and the occurrence of dysphagia.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.272
Threshold uncertainty score0.321

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.034
GPT teacher head0.386
Teacher spread0.352 · 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 teacher head, 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

Citations1
Published2023
Admission routes1
Has abstractyes

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