MétaCan
Menu
Back to cohort
Record W2614676745 · doi:10.1055/s-0037-1603345

Is the Correlation between Salivary Cortisol and Serum Cortisol Reliable Enough to Enable Use of Salivary Cortisol Levels in Preterm Infants?

2017· article· en· W2614676745 on OpenAlexaff
Katherine E. Wynne‐Edwards, Parthiv Amin, Ruokun Zhou, Arun Sundaram, Tania Martinez-Soto, Danièle Pacaud, Harish Amin

Bibliographic record

VenueAmerican Journal of Perinatology · 2017
Typearticle
Languageen
FieldMedicine
TopicAdrenal Hormones and Disorders
Canadian institutionsUniversity of Calgary
FundersHealth Research Board
KeywordsSalivaMedicineInternal medicineAdrenal insufficiencyHydrocortisoneGestational ageEndocrinologyGlucocorticoidCorrelationPhysiologyPregnancyBiology

Abstract

fetched live from OpenAlex

Background Newborn premature infants are susceptible to development of relative adrenal insufficiency following transition from fetal to extrauterine life. However, the best diagnostic test for adrenal insufficiency in neonates has yet to be developed. Objectives and Methods The aim of this study was (1) to assess the feasibility of obtaining sufficient saliva sample to allow measurement of cortisol by liquid chromatography coupled to tandem mass spectrometry and (2) to assess the correlation, if any, between salivary and serum cortisol in preterm infants of ≤32 weeks' gestational age at birth. Results Samples for 230 paired serum and saliva cortisol levels from 90 preterm infants were analyzed. 87.5% of samples collected had sufficient salivary volumes for measurement. Despite being statistically significant (p < 0.0001), the correlation (Spearman r = 0.674) between serum and salivary cortisol was not strong. Conclusion Salivary free cortisol measurement is feasible but cannot be used to accurately reflect serum total cortisol. Further studies comparing salivary free cortisol to serum free cortisol and establishing normative data are needed before salivary cortisol can be used for diagnostic purposes.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.671

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.037
GPT teacher head0.319
Teacher spread0.282 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueAmerican Journal of PerinatologySame topicAdrenal Hormones and DisordersFrench-language works237,207