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Record W4404416405 · doi:10.14740/jnr769

Prevalence, Clinical Profile and Influence of Onset-to-Treatment Time for Subarachnoid Hemorrhage on Quality of Life in Patients: A Retrospective Study of a Decade

2024· article· en· W4404416405 on OpenAlexvenueno aff
Glauber Luan Lopes Guimarães, Caio Bolfer da Silva, Enrico Garcia Panucci, Rodrigo Ferrari Fernandes Naufal, Adib Saráty Malveira, Lorenna Izadora Capovilla Martins Gonzalez-Reyes, Marcos Natal Rufino, Margarete Oliveira

Bibliographic record

VenueJournal of Neurology Research · 2024
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSubarachnoid hemorrhageRetrospective cohort studyPediatricsSurgery

Abstract

fetched live from OpenAlex

Background: The objective of the current study was to outline the clinical profile of patients diagnosed with subarachnoid hemorrhage (SAH) due to intracranial aneurysm rupture, treated at a reference hospital in the interior of Sao Paulo, and to investigate the time elapsed between the ictus and the surgical approach and its relationship with motor deficits evaluated according to the modified Rankin Scale (MRS). Methods: This is a retrospective cohort, with data collection and analysis of patients with SAH between 2010 and 2020. Results: The results showed no correlation between MRS and: sex (P = 0.3459 nonparametric Mann-Whitney test), age range, and ethnicity (P = 0.5451 and P = 0.513, respectively, nonparametric Kruskal-Wallis test), affected arteries (P = 0.4801 nonparametric test of Kruskal-Wallis), and onset-to-treatment time for hemorrhage (rho = 0.02; P = 0.8204 in Spearman's nonparametric correlation test), adopting 5% significance. Conclusions: The prevalence of SAH in the hospital studied, and the profile and clinical aspects of the patients treated are similar to existing data in the literature. The onset-to-treatment time and site of the aneurysm did not influence the prognosis of the patients, who presented slightly better MRS levels than those found in the literature. J Neurol Res. 2024;14(2):68-73 doi: https://doi.org/10.14740/jnr769

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)0.004
metaresearch head score (Gemma)0.005
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 score0.063
Threshold uncertainty score0.631

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.088
GPT teacher head0.442
Teacher spread0.353 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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