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Record W7162135962 · doi:10.82308/4162

Feeding practices in open abdomen following laparotomy: an assessment of nutrition adequacy and clinical outcomes

2016· dissertation· en· W7162135962 on OpenAlexaboutno aff
M. Hassan

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldMedicine
TopicAbdominal Surgery and Complications
Canadian institutionsnot available
Fundersnot available
KeywordsAbdomenEnergy expenditureParenteral nutritionClinical nutritionClosure (psychology)Resting energy expenditureProspective cohort studyRetrospective cohort studyMalnutrition

Abstract

fetched live from OpenAlex

Proper nutrition can promote healing and improve clinical outcome. Open abdomen patients are at risk of declining nutritional status. To investigate this issue, we first assessed nutrition adequacy and clinical outcomes by using data from a retrospective study on 33 patients. We assessed potential factors as correlates of altering resting energy expenditure by using data from a prospective pilot study of 7 open abdomen patients, after implementing indirect calorimetry as a standard of care at a Level-1 trauma center in Montreal. At baseline, the vast majority of the participants were underfed in the first study, while in the second study unexpected dynamic changes in resting energy expenditure were observed after closure of the abdomen compared to before closure of the abdomen. The findings of this research highlight the need for large multi-center studies in order to better understand nutritional targets and nutritional risks for open abdomen patients.

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)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.119
GPT teacher head0.536
Teacher spread0.417 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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