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Record W7007924242

Apendicectomía pasada como factor de riesgo para deterioro cognitivo en población adulta

2023· dissertation· es· W7007924242 on OpenAlexaboutno aff

Bibliographic record

Venuerenati · 2023
Typedissertation
Languagees
FieldMedicine
TopicAppendicitis Diagnosis and Management
Canadian institutionsnot available
Fundersnot available
KeywordsCognitive impairmentRisk factorCognitionIncidence (geometry)
DOInot available

Abstract

fetched live from OpenAlex

Objetivo: Determinar si la apendicectomía pasada es un factor de riesgo para deterioro cognitivo en adultos de 50 a 70 años.
\nMétodo: Se efectuó un estudio de casos y controles con 270 pacientes del Hospital Regional Docente de Trujillo entre mayo y julio de 2023. Se utilizaron 90 casos (con deterioro cognitivo) y 180 controles (sin deterioro), diagnosticados mediante la Evaluación Cognitiva Montreal (MoCA).
\nResultados: Del total con deterioro cognitivo, el 31,11% había tenido apendicectomía, con un promedio de 25 años desde la cirugía. En contraste, solo el 3,33% sin deterioro reportó apendicectomía previa. Respecto a otras cirugías: el 40% con deterioro tuvo colecistectomía y el 23,33% reportó otras operaciones. El análisis reveló diferencias significativas en edad, hipertensión, diabetes y tabaquismo entre los grupos. Sin embargo, no hubo diferencia significativa por género. El análisis de regresión logística destacó que la edad y la apendicectomía pasada estaban fuertemente asociadas al deterioro cognitivo, con un ORa de 1,20 y 12,91 respectivamente. También se encontró relación con colecistectomía, otras cirugías y tabaquismo.
\nConclusión: La apendicectomía es un factor de riesgo significativo para el deterioro cognitivo en adultos entre 50 y 70 años

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.706
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.003

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 head0.025
GPT teacher head0.331
Teacher spread0.306 · 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; both teacher heads agree on what is shown here.

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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