Evidence map›Paper›PMID 41392165›Full record

ReviewDiagnostic and prognostic research2025

Risk of bias in machine learning and statistical models to predict height or weight: a systematic review in fetal and paediatric medicine.

Neil R Lawrence, Irina Bacila, Joseph Tonge, Anthea Tucker, Jeremy Dawson, Zi-Qiang Lang, Nils P Krone, Paula Dhiman, Gary S Collins

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Diagnostic and prognostic research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Neil R Lawrence *Division of Clinical Medicine, University of Sheffield, Sheffield, S10 2RX, UK. n.r.lawrence@sheffield.ac.uk.
Irina Bacila *Division of Clinical Medicine, University of Sheffield, Sheffield, S10 2RX, UK.
Joseph TongeDivision of Clinical Medicine, University of Sheffield, Sheffield, S10 2RX, UK.
Anthea TuckerHealth Sciences Library, University of Sheffield, Sheffield, S10 2RX, UK.
Jeremy DawsonDivision of Population Health, University of Sheffield, Sheffield, S10 2RX, UK.
Zi-Qiang LangDepartment of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, S1 3JD, UK.
Nils P KroneDivision of Clinical Medicine, University of Sheffield, Sheffield, S10 2RX, UK.
Paula DhimanCentre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK.
Gary S CollinsDepartment of Applied Health Sciences, University of Birmingham, Birmingham, B15 2TT, United Kingdom.

Funding

Cancer Research UK C49297/A27294National Institute for Health Research Collaboration for Leadership in Applied Health Research and Care Yorkshire and Humber 302559
6 · The paper itself

Abstract

backgroundPrediction of suboptimal growth allows early intervention that can improve outcomes for developing fetus' as well as infants and children. We investigate the risk of bias in statistical or machine learning models to predict the height or weight of a fetus, infant or child under 20 years of age to inform the current standard of research and provide insight into why equations developed over 30 years ago are still recommended for use by national professional bodies.

methodsWe systematically searched MEDLINE and EMBASE for peer reviewed original research studies published in 2022. We included studies if they developed or validated a multivariable model to predict height or weight of an individual using two or more variables, excluding studies assessing imaging or using genetics or metabolomics information. Risk of bias was assessed for all prediction models and analyses using the Prediction model Risk Of Bias ASsessment Tool (PROBAST).

resultsSixty-four studies were included, in which we assessed the development of 180 models and validation of 61 models. Sample size was only considered in 10% of developed models and 13% of validated models. Despite height and weight being continuous variables, 77% of models developed predicted a dichotomised outcome variable. REGISTRATION: The review was registered on PROSPERO (ID: CRD42023421146), the International prospective register of systematic reviews on 26/4/2023.

Indexed as

GrowthHeightMachine learningObstetricsPaediatricsPrediction modellingRisk of biasStatistical modellingSystematic reviewWeight

Identifiers

PMID41392165
PMCPMC12703889

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.