MétaCan
Menu
Back to cohort
Record W2132518211 · doi:10.1515/jpm.2008.054

The fetus, not the mother, elicits maternal immunologic rejection: lessons from discordant dizygotic twin placentas

2008· article· en· W2132518211 on OpenAlexaff
Kamran Yusuf, Harvey J. Kliman

Bibliographic record

VenueJournal of Perinatal Medicine · 2008
Typearticle
Languageen
FieldMedicine
TopicPregnancy and preeclampsia studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPlacentaMedicineDizygotic twinsTwin PregnancyChorionic villiFetusDizygotic twinObstetricsPregnancyAndrologyPhysiologyImmunologyTwin studyBiologyGenetics

Abstract

fetched live from OpenAlex

AIMS: Our objective was to elucidate the pathogenesis of twin discordance in four dizygotic pregnancies where only one of the twins had IUGR due to chronic villitis. METHODS: We identified four cases of dizygotic twin placentas over a period of four years with evidence of chronic villitis. There was no clinical or pathologic evidence of TORCH, bacterial infection, preeclampsia or autoimmune disorders. Placentas were weighed, processed for histologic examination and stained with CD45RO (clone UCHL1) mouse monoclonal antibody, which identifies T-cells. RESULTS: All placentas were dichorionic, with two being fused. Birth weight differences were 29%, 41%, 17% and 10%. Villitis was more marked in the placenta of the twin that weighed less and correlated with the degree of weight discordance. On examining the junction between the fused dichorionic placentas, the chorionic villi from the smaller twin contained numerous T-cells, whereas the villi associated with the less affected twin, showed little to no T-cells. CONCLUSION: We describe a series of dizygotic twin placentas where the more severe the chronic villitis, the more affected the placenta and fetus. Since the maternal environment was constant for each of these twins, differences in villitis severity appears to be attributable to differences in the ability of each placenta to induce a maternal immune response.

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)0.001
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 score0.541
Threshold uncertainty score0.523

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.040
GPT teacher head0.301
Teacher spread0.261 · 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

Citations19
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Perinatal MedicineSame topicPregnancy and preeclampsia studiesFrench-language works237,207