MétaCan
Menu
Back to cohort
Record W4224325577 · doi:10.1002/ansa.202100055

Artificial amniotic fluid for nuclear magnetic resonance spectroscopy studies

2022· article· en· W4224325577 on OpenAlexaff
Doshina Naila, Siddharth Sadanand, Dafna Sussman

Bibliographic record

VenueAnalytical Science Advances · 2022
Typearticle
Languageen
FieldMedicine
TopicNeonatal and fetal brain pathology
Canadian institutionsUniversity of TorontoToronto Metropolitan UniversitySt. Michael's Hospital
Fundersnot available
KeywordsAmniocentesisImaging phantomAmniotic fluidMiscarriageMagnetic resonance imagingNuclear magnetic resonance spectroscopyNuclear magnetic resonanceMedicineFetusPrenatal diagnosisPregnancyNuclear medicineRadiologyPhysicsBiologyGenetics

Abstract

fetched live from OpenAlex

Amniocentesis is the process of retrieving the nutrient-rich amniotic fluid (AF) that encompasses the growing fetus in order to diagnose fetal diseases and developmental disorders. Currently, it is only performed on pregnant persons at risk and is invasive with the potential for infection and in some cases, miscarriage. A non-invasive alternative is needed and could be developed using magnetic resonance spectroscopy (MRS). To develop such MRS sequences, ample testing and training are needed and could be most efficiently conducted on a phantom. We propose a protocol for creating such a synthetic AF phantom for MRS testing and optimization. The proposed AF is validated using nuclear magnetic resonance (NMR) proving it produces spectra comparable to those in the literature. The results from this study can aid in developing a non-invasive fetal diagnostic tool to replace amniocentesis.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
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 science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.029
GPT teacher head0.343
Teacher spread0.314 · 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 designBench or experimental
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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueAnalytical Science AdvancesSame topicNeonatal and fetal brain pathologyFrench-language works237,207