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Record W613036546 · doi:10.1530/edm-15-0001

Pituitary injury and persistent hypofunction resulting from a peripartum non-hemorrhagic, vaso-occlusive event

2015· article· en· W613036546 on OpenAlexaff
Anita Kuriya, David V. Morris, Michael H. Dahan

Bibliographic record

VenueEndocrinology Diabetes and Metabolism Case Reports · 2015
Typearticle
Languageen
FieldMedicine
TopicNeurological Complications and Syndromes
Canadian institutionsMcGill University Health CentreMcGill University
Fundersnot available
KeywordsVasospasmMedicineBromocriptinePituitary apoplexyPreeclampsiaHypopituitarismInternal medicineCardiologyPituitary glandInfarctionEndocrinologyAnesthesiaSubarachnoid hemorrhageProlactinPregnancyHormoneBiologyPituitary adenoma

Abstract

fetched live from OpenAlex

UNLABELLED: Cerebral vascular accidents are caused by vasospasm when induced by preeclampsia or by dopamine agonists. However, six arteries nourish the pituitary and prevent against vasospasm-induced damage, which up until now has not been thought to occur. Bromocriptine was used to arrest lactation in a 31-year-old with secondary amenorrhea following preeclampsia and fetal demise at 28 weeks gestation. Tests and history revealed panhypopituitarism not associated with hemorrhage or mass infarction but instead caused by vasospasm. The present study is the first report of pituitary damage from a non-hemorrhagic, vaso-occlusive event in the literature. In keeping with Sheehan's and Simon's syndromes, we have named pituitary damage resulting from vaso-occlusion as Dahan's syndrome, and a literature review suggests that it may be a common and previously overlooked disorder. LEARNING POINTS: Vasospasm can cause damage to the pituitary gland, although it was not previously believed to do so.Preeclampsia and the use of a dopamine agonist, particularly in the peripartum state, may trigger vasospasm.Vasospasm resulting from dopamine agonists may be a common cause of injury to the pituitary gland, and it may have been overlooked in the past.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.168
Threshold uncertainty score0.768

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 head0.022
GPT teacher head0.265
Teacher spread0.243 · 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

Citations5
Published2015
Admission routes1
Has abstractyes

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