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Record W4384342882 · doi:10.18231/j.ijnmhs.2023.016

Case report on nutrition management in liver transplant

2023· article· en· W4384342882 on OpenAlexaff
Edwina Raj, Pravalika Londe, Mallikarjun Sakpal, Sonal Asthana, Simran Khanam

Bibliographic record

VenueIP Journal of Nutrition Metabolism and Health Science · 2023
Typearticle
Languageen
FieldMedicine
TopicLiver Disease and Transplantation
Canadian institutionsASTER
Fundersnot available
KeywordsMedicineLiver transplantationIntervention (counseling)Psychological interventionIntensive care medicineDietary managementCalorieTransplantationSurgeryInternal medicineNursing

Abstract

fetched live from OpenAlex

Liver transplantation or hepatic transplantation is a procedure of replacing diseased liver with a healthy liver. It needs cautious post-operative care including nutritional intervention. We present a 65 year old patient underwent a liver transplant due to acute liver failure. The patient’s diet history revealed a daily intake of carbohydrates and fats but inadequate protein intake. The patient’s nutritional interventions were analyzed and evaluated through hospital recalls and proper follow up visits. In studies, hospital recall on 7 day of post-surgery showed a great improvement in calorie and protein intake. The discharge diet plan included a well-balanced diet with protein, carbohydrates and fat intake to ensure nutritional care for the patient’s recovery. Nutritional intervention plays a vital role in post-operative care of liver transplant patients. This case study signifies the importance of personalized dietary intervention to resolve PEM, improve nutritional intake while supporting successful surgical outcome and recovery .

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)0.000
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: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.003
Science and technology studies0.0050.002
Scholarly communication0.0020.003
Open science0.0010.003
Research integrity0.0060.006
Insufficient payload (model declined to judge)0.0050.001

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 head0.062
GPT teacher head0.358
Teacher spread0.296 · 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 designCase report
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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Same venueIP Journal of Nutrition Metabolism and Health ScienceSame topicLiver Disease and TransplantationFrench-language works237,207