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Record W7095747044

The skin in pregnancy

2016· article· en· W7095747044 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicNeurological Complications and Syndromes
Canadian institutionsnot available
Fundersnot available
KeywordsPregnancyEtiologyStroke (engine)Subarachnoid hemorrhageInfarctionRetrospective cohort studyCerebral infarction
DOInot available

Abstract

fetched live from OpenAlex

Background and Purpose—We sought to characterize the subtypes of stroke associated with pregnancy and the puerperium, with emphasis on timing, etiology, risk factors, and outcome. Methods—We conducted a retrospective analysis of patients admitted to the Toronto Hospital between January 1, 1980, and June 30, 1997, with a diagnosis of stroke during pregnancy or within 6 weeks postpartum. Strokes were classified as ischemic (arterial or venous) or hemorrhagic (subarachnoid or intracerebral). All patients were investigated with at least a CT scan of the head, and most had MRI and/or cerebral angiography. Results—Of approximately 50 700 admissions for delivery, 34 patients with a diagnosis of stroke were identified (21 infarctions and 13 hemorrhages). Of patients with infarction, 13 were arterial and 8 were venous. Nine of 13 arterial events occurred in the third trimester or puerperium. Seven of 8 venous occlusions occurred postpartum. An etiologic diagnosis was made in 7 of 13 patients with arterial territory infarction, including cardiac emboli, coagulopathies, and carotid artery dissection. Of patients with hemorrhage, 7 were subarachnoid and 6 were intracerebral. The etiology was identified in 10 patients: 3 were due to ruptured aneurysms, 5 were associated with arteriovenous malformations, and 2 were associated with disseminated intravascular coagulation. All patients with infarction survived, but 3 patients with hemorrhage died. Conclusions—The majority of strokes associated with pregnancy were arterial occlusions. Most presented during the third

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.768
Threshold uncertainty score0.197

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.030
GPT teacher head0.286
Teacher spread0.257 · 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
Published2016
Admission routes1
Has abstractyes

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