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Accidentes vasculares cerebrales en la comarca de Osona: Factores de riesgo cardiovascular

2004· article· es· W2010780833 on OpenAlexaff
C. Sanclemente Ansó, F. Alonso Valdés, E. Rovira Pujol, D. Vigil Martín, J. Vilaró Pujals

Bibliographic record

VenueAnales de Medicina Interna · 2004
Typearticle
Languagees
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsVictoria General Hospital
Fundersnot available
KeywordsMedicineBoroughIncidence (geometry)Emergency medicineDiabetes mellitusRisk factorNeurosurgeryPediatricsInternal medicineSurgery

Abstract

fetched live from OpenAlex

INTRODUCTION: Cerebrovascular disease (CVD) is the second cause of hospitalization in the order of frequency in our service, thus reflecting this pathologýs high incidence in our borough. This study analyzes clinical and biological characteristics, cardiovascular risk factors and actions taken in hospital discharge in the internal medicine department of a borough hospital (the reference hospital in Osona borough), as well as other characteristics of the hospitalized CVD patients from January 2001 to December 2001. METHOD: This study was performed by revising each patient's hospital discharge report. 277 patients were hospitalized for CVD. RESULTS: Biological and demographic characteristics, as well as cardiovascular risk factors analyzed (arterial hypertension, diabetes, smoking, or dyslipidemy) were similar to other series. Incidence of hemorrhagic and cardio-embolic CVD was slightly lower, taken into account that hemorrhagic episodes that needed neurosurgical intervention were transferred to a higher-level center with a department of neurosurgery. Age was neither a factor for bad prediction leading to bad sequels after the episode nor a cause of an increased mortality. This data differs from others series. CONCLUSION: Actions taken on discharge, on cardiovascular risk factors and on hygienic-dietetic recommendations were deficient. Average stay was higher when compared to average stay in specialized ictus units.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.378
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.008
GPT teacher head0.260
Teacher spread0.252 · 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.

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

Citations11
Published2004
Admission routes1
Has abstractyes

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