MétaCan
Menu
Back to cohort
Record W4391612220 · doi:10.1080/10408363.2024.2306804

Challenges of insulin-like growth factor-1 testing

2024· review· en· W4391612220 on OpenAlexaff
Rongrong Huang, Junyan Shi, Ruhan Wei, Jieli Li

Bibliographic record

VenueCritical Reviews in Clinical Laboratory Sciences · 2024
Typereview
Languageen
FieldMedicine
TopicGrowth Hormone and Insulin-like Growth Factors
Canadian institutionsVancouver General HospitalUniversity of British ColumbiaVancouver Coastal Health
Fundersnot available
KeywordsInsulin-like growth factorFactor (programming language)InsulinComputer scienceGrowth factorBiologyComputational biologyEndocrinologyGeneticsProgramming language

Abstract

fetched live from OpenAlex

Insulin-like growth factor 1 (IGF-1), primarily synthesized in the liver, was initially discovered due to its capacity to replicate the metabolic effects of insulin. Subsequently, it emerged as a key regulator of the actions of growth hormone (GH), managing critical processes like cell proliferation, differentiation, and apoptosis. Notably, IGF-1 displays a longer half-life compared to GH, making it less susceptible to factors that may affect GH concentrations. Consequently, the measurement of IGF-1 proves to be more specific and sensitive when diagnosing conditions such as acromegaly or GH deficiency. The recognition of the existence of IGFBPs and their potential to interfere with IGF-1 immunoassays urged the implementation of various techniques to moderate this issue and provide accurate IGF-1 results. Additionally, in response to the limitations associated with IGF-1 immunoassays and the occurrence of discordant IGF-1 results, modern mass spectrometric methods were developed to facilitate the quantification of IGF-1 levels. Taking advantage of their ability to minimize the interference caused by IGF-1 variants, mass spectrometric methods offer the capacity to deliver robust, reliable, and accurate IGF-1 results, relying on the precision of mass measurements. This also enables the potential detection of pathogenic mutations through protein sequence analysis. However, despite the analytical challenges, the discordance in IGF-1 reference intervals can be attributed to a multitude of factors, potentially leading to distinct interpretations of results. The establishment of reference intervals for each assay is a demanding task, and it requires nationwide multicenter collaboration among laboratorians, clinicians, and assay manufacturers to achieve this common goal in a cost-effective and resource-efficient manner. In this comprehensive review, we examine the challenges associated with the standardization of IGF-1 measurement methods, the minimization of pre-analytical factors, and the harmonization of reference intervals. Particular emphasis will be placed on the development of IGF-1 measurement techniques using "top-down" or "bottom-up" mass spectrometric methods.

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)0.009
metaresearch head score (Gemma)0.076
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.901
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.076
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0070.001
Bibliometrics0.0010.004
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0000.001

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 head0.403
GPT teacher head0.510
Teacher spread0.106 · 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 designOther design
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

Citations21
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueCritical Reviews in Clinical Laboratory SciencesSame topicGrowth Hormone and Insulin-like Growth FactorsFrench-language works237,207