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Record W4361263614 · doi:10.3399/bjgp23x732309

Inequalities in the assessment of childhood short stature

2023· editorial· en· W4361263614 on OpenAlexaff
Justin H. Davies, Jenny Child, Joseph Freer, Helen L. Storr

Bibliographic record

VenueBritish Journal of General Practice · 2023
Typeeditorial
Languageen
FieldMedicine
TopicBirth, Development, and Health
Canadian institutionsNovelis (Canada)
FundersNational Institute for Health and Care Research
KeywordsShort statureMedicinePediatricsInequality

Abstract

fetched live from OpenAlex

Measurement of height is a key part of the assessment of a child's health.Short stature is pragmatically defined as height less than the second centile, plotted on sex-specific growth charts.Although the commonest cause of short stature is familial, it may be the earliest and/or only presenting feature of serious childhood illness and therefore requires investigation (see Clinical Practice article in this BJGP Issue). 1 In some cases, short stature is a marker of socioeconomic vulnerability; in England it strongly tracks to areas with higher levels of deprivation. 2Early detection of short stature is key to optimising health outcomes.Delayed diagnosis can result in reduced adult height and failure to address the underlying pathology can have potentially serious consequences.Short stature in young children may also be an important predictor of future cognition. 3hort stature is often associated with significant child and caregiver anxiety.The child may experience low self-esteem, anxiety, and bullying at school, exacerbating the situation:

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)0.006
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.362
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.004
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 head0.030
GPT teacher head0.378
Teacher spread0.348 · 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 designNot applicable
Domainnot available
GenreEditorial

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

Citations5
Published2023
Admission routes1
Has abstractyes

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