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Record W1964675387 · doi:10.5867/medwave.2014.11.6058

Does chewing gum accelerate recovery after abdominal surgery?

2014· article· en· W1964675387 on OpenAlexaff
Gabriel Rada, José Viñuela

Bibliographic record

VenueMedwave · 2014
Typearticle
Languageen
FieldMedicine
TopicEnhanced Recovery After Surgery
Canadian institutionsCochrane
Fundersnot available
KeywordsMedicineChewing gumPeer reviewDentistrySurgeryGeneral surgeryNursingFood science

Abstract

fetched live from OpenAlex

El íleo postoperatorio es una condición común que retrasa la recuperación luego de una cirugía abdominal.El uso precoz de goma de mascar, como método de alimentación fingida, estimularía la peristalsis permitiendo una alimentación más temprana.Utilizando la base de datos Epistemonikos, la cual es mantenida mediante búsquedas en 20 bases de datos, identificamos 14 revisiones sistemáticas que en conjunto incluyen 24 estudios aleatorizados.Realizamos un metanálisis y tablas de resumen de los resultados utilizando el método GRADE.Concluimos que la goma de mascar probablemente disminuye el tiempo de hospitalización luego de una cirugía abdominal.

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)0.001
metaresearch head score (Gemma)0.003
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: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.014
GPT teacher head0.237
Teacher spread0.224 · 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".

Quick stats

Citations1
Published2014
Admission routes1
Has abstractyes

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