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Record W2895955515 · doi:10.1136/bmjpo-2018-000303

New anthropometric classification scheme of preoperative nutritional status in children: a retrospective observational cohort study

2018· article· en· W2895955515 on OpenAlexaff
Anne M. Stey, Joni Ricks‐Oddie, Sheila M. Innis, Shawn J. Rangel, R. Lawrence Moss, Bruce L. Hall, Albert W. Dibbins, Erik D. Skarsgard

Bibliographic record

VenueBMJ Paediatrics Open · 2018
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsBC Children's HospitalUniversity of British Columbia
Fundersnot available
KeywordsUnderweightMedicineOverweightBody mass indexAnthropometryCohortRetrospective cohort studyMalnutritionWastingLogistic regressionObservational studyPediatricsOdds ratioSurgeryInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: WHO uses anthropometric classification scheme of childhood acute and chronic malnutrition based on low body mass index (BMI) ('wasting') and height for age ('stunting'), respectively. The goal of this study was to describe a novel two-axis nutritional classification scheme to (1) characterise nutritional profiles in children undergoing abdominal surgery and (2) characterise relationships between preoperative nutritional status and postoperative morbidity. DESIGN: This was a retrospective observational cohort study. SETTING: The setting was 50 hospitals caring for children in North America that participated in the American College of Surgeons National Surgical Quality Improvement Program Paediatric from 2011 to 2013. PARTICIPANTS: Children >28 days who underwent major abdominal operations were identified. INTERVENTIONS/MAIN PREDICTOR: The cohort of children was divided into five nutritional profile groups based on both BMI and height for age Z-scores: (1) underweight/short, (2) underweight/tall, (3) overweight/short, (4) overweight/tall and (5) non-outliers (controls). MAIN OUTCOME MEASURES: Multiple variable logistic regressions were used to quantify the association between 30-day morbidity and nutritional profile groups while adjusting for procedure case mix, age and American Society of Anaesthesiologists class. RESULTS: A total of 39 520 cases distributed as follows: underweight/short (656, 2.2%); underweight/tall (252, 0.8%); overweight/short (733, 2.4%) and overweight/tall (1534, 5.1%). Regression analyses revealed increased adjusted odds of composite morbidity (35%) and reintervention events (75%) in the underweight/short group, while overweight/short patients had increased adjusted odds of composite morbidity and healthcare-associated infections (43%), and reintervention events (79%) compared with controls. CONCLUSION: Stratification of preoperative nutritional status using a scheme incorporating both BMI and height for age is feasible. Further research is needed to validate this nutritional risk classification scheme for other surgical procedures in children.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.031
Threshold uncertainty score0.782

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.103
GPT teacher head0.425
Teacher spread0.322 · 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.

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

Citations3
Published2018
Admission routes1
Has abstractyes

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