MétaCan
Menu
Back to cohort
Record W2084198337 · doi:10.1155/2010/370692

Spuriously Elevated Serum IGF-1 in Adult Individuals with Delayed Puberty: A Diagnostic Pitfall

2010· article· en· W2084198337 on OpenAlexaff
Syed Ali Imran, Michael Pelkey, David B. Clarke, Dale H. Clayton, Peter Trainer, Shereen Ezzat

Bibliographic record

VenueInternational Journal of Endocrinology · 2010
Typearticle
Languageen
FieldMedicine
TopicGrowth Hormone and Insulin-like Growth Factors
Canadian institutionsUniversity of TorontoDalhousie University
Fundersnot available
KeywordsMedicineInternal medicineEndocrinologyDelayed pubertyTestosterone (patch)Insulin-like growth factorAndrogenGrowth hormoneBone ageHormoneGrowth factorReceptor

Abstract

fetched live from OpenAlex

Serum insulin-like growth factor-1 (IGF-1) is a sensitive marker of growth hormone (GH) activity. The levels of IGF-1 vary widely, peaking during puberty and declining with advancing age. During adolescence, serum IGF-1 levels tend to correlate better with pubertal stage rather than chronological age. Here we discuss two cases of delayed puberty, both in their 20s, who presented with high serum IGF-1 but no clinical or biochemical evidence of hypersomatotropism as confirmed by appropriate GH response to an oral glucose challenge. Both individuals achieved full pubertal status with testosterone replacement therapy and their serum IGF-1 levels settled into normal age-specific range. We suggest that in chronologically adult individuals with delayed puberty, serum IGF-1 should not be interpreted on the basis of age-specific normal values but rather on their pubertal status. Furthermore, in the absence of another cause of elevated IGF-1, the expectation is that IGF-1 levels will decline towards age-normative ranges following androgen replacement therapy.

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)0.001
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.001
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 head0.008
GPT teacher head0.262
Teacher spread0.255 · 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

Citations3
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of EndocrinologySame topicGrowth Hormone and Insulin-like Growth FactorsFrench-language works237,207