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Record W3194508029 · doi:10.3389/fcimb.2021.660983

Inflammatory Amplification: A Central Tenet of Uterine Transition for Labor

2021· review· en· W3194508029 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueFrontiers in Cellular and Infection Microbiology · 2021
Typereview
Languageen
FieldMedicine
TopicPregnancy and Medication Impact
Canadian institutionsCentre Hospitalier Universitaire Sainte-JustineUniversity of Alberta
FundersChildren's Hospital FoundationStollery Children’s Hospital FoundationCanadian Institutes of Health ResearchWomen and Children's Health Research InstituteChildren's Health Research Institute
KeywordsTransition (genetics)MedicineBiologyGenetics

Abstract

fetched live from OpenAlex

In preparation for delivery, the uterus transitions from actively maintaining quiescence during pregnancy to an active parturient state. This transition occurs as a result of the accumulation of pro-inflammatory signals which are amplified by positive feedback interactions involving paracrine and autocrine signaling at the level of each intrauterine cell and tissue. The amplification events occur in parallel until they reach a certain threshold, 'tipping the scale' and contributing to processes of uterine activation and functional progesterone withdrawal. The described signaling interactions all occur upstream from the presentation of clinical labor symptoms. In this review, we will: 1) describe the different physiological processes involved in uterine transition for each intrauterine tissue; 2) compare and contrast the current models of labor initiation; 3) introduce innovative models for measuring paracrine inflammatory interactions; and 4) discuss the therapeutic value in identifying and targeting key players in this crucial event for preterm birth.

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.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.986
Threshold uncertainty score0.729

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 studies0.0000.000
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 head0.020
GPT teacher head0.302
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