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Record W4408026526 · doi:10.1002/jmd2.70000

Psychosocial Challenges Facing Young People With Inherited Metabolic Disorders and Their Parents: A Systematic Review

2025· review· en· W4408026526 on OpenAlexfundno aff
Clara Sherlock, Kim Clarke, Norah Jordan

Bibliographic record

VenueJIMD Reports · 2025
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism and Genetic Disorders
Canadian institutionsnot available
FundersChildren's Health Foundation
KeywordsPsychosocialPsychologyMedicinePsychiatryClinical psychology

Abstract

fetched live from OpenAlex

Recent advancements in new-born screening have reduced the risk of life-threatening complications associated with inherited metabolic disorders. However, the risk of negative psychosocial effects on families persists. The aim of the present study was to systematically review the literature concerning the psychosocial challenges experienced by young people with metabolic conditions and their families, to inform the development of supports that meet the needs of those linked with metabolic services. The electronic databases MEDLINE, CINAHL, PsychInfo, and Psychology and Behavioural Sciences Collection were searched for studies examining psychosocial challenges reported by families with inherited metabolic conditions, over the last two decades. Five-thousand sixty-seven articles were screened for relevance. Twenty-nine studies met the inclusion criteria. Study quality and reliability were independently assessed by two reviewers. Results highlighted the myriad of physical, social, psychological and practical challenges experienced by young people with metabolic conditions and their families. These challenges included social isolation, burden of care, and learning and emotional difficulties. Findings reiterate the importance of developing peer support groups and delivering psychoeducation to families, as well as the central role psychology and social work should play in metabolic MDTs, to improve families' experiences and outcomes.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.736
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.277
Teacher spread0.266 · 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.

Study designSystematic review
Domainnot available
GenreReview

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
Published2025
Admission routes1
Has abstractyes

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