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Record W4235728346 · doi:10.32920/ryerson.14655213.v1

3D printed phantom to mimic dynamic softening of cervix during pre-labour

2021· preprint· en· W4235728346 on OpenAlexaff
Jung S. Kim

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicMaternal and Perinatal Health Interventions
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsImaging phantomMaterials scienceElastomerCervixSofteningStress (linguistics)Constant (computer programming)Thermoplastic polyurethaneComposite materialBiomedical engineeringMedicineNuclear medicineComputer science

Abstract

fetched live from OpenAlex

It is thought that through the development of more realistic training models for midwives and obstetricians it may be possible to reduce the overuse of labour induction. To this end we demonstrate a method for creating pneumatically-controlled phantom cervixes using thermoplastic elastomer, filled with a granular material. The maximum spring constant of the phantom cervix was measured to be 10.5 N/m at -20 kPa deflated air (vacuum) and the minimum spring constant measured was 5.3 N/m at 20 kPa inflated air. The true stress measured on these elastomeric phantom cervixes indicated a maximum stress of 133 kPa and a minimum stress of 94 kPa at 0.15 strain. Discrimination and threshold tests demonstrated that people can distinguish between the hard and soft states of the phantom. Future work will focus on increasing the softness of these devices.

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)0.000
metaresearch head score (Gemma)0.001
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: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.022
GPT teacher head0.356
Teacher spread0.334 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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