Evidence map›Paper›PMID 41589173›Full record

ArticleCureus2025

Craniofacial and Radiological Features as Diagnostic Clues to Unmask Acromegaly: A Case Report.

Jhalak Modi, Eiman Ibrahim, Katherine LaFoe, Tushar Ralhan, Esra Demirtas

Abstract readCase Reports
In one paragraph

Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Jhalak ModiInternal Medicine, University of Missouri, Columbia, USA.
Eiman IbrahimEndocrinology, University of Missouri, Columbia, USA.
Katherine LaFoeRadiology, University of Missouri, Columbia, USA.
Tushar RalhanInternal Medicine, University of Missouri, Columbia, USA.
Esra DemirtasEndocrinology and Diabetes, University of Missouri, Columbia, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A 70-year-old female with a past medical history of type 2 diabetes mellitus (treated with insulin), hypertension, and bipolar disorder was admitted to the Intensive Care Unit after suffering cardiac arrest post-seizure. During the initial workup, CT of the head was notable for marked, diffuse calvarial thickening (~17 mm), a large sella turcica, and no evidence of pituitary apoplexy. MRI of the head showed a 1.9 cm pituitary macroadenoma. The physical examination was notable for thick facial features such as macroglossia, acrochordon, and fleshy hands - consistent with acromegaly. Insulin-like growth factor-1 (IGF-1) was elevated at 570 ng/mL (Z score: 4; normal 34-245). IGF-binding protein 3 (IGFBP-3) was elevated at 9 µg/mL (normal 3-6). However, the patient had never previously been diagnosed with acromegaly. Throughout the course of her admission, the patient required multiple bronchoscopies due to thick secretions and mucus plugging. The etiology of her respiratory failure and sudden cardiac arrest remained undetermined after a comprehensive diagnostic workup. While acromegaly typically manifests with musculoskeletal changes and endocrine abnormalities - including diabetes mellitus, hypertension, and galactorrhea - it also carries a substantial risk of secondary respiratory and cardiac complications. This report highlights the importance of early diagnosis of acromegaly, specifically in identifying, being cognizant of, and preventing respiratory and cardiac complications.

Indexed as

acromegaly comorbiditiesacromegaly symptomsawareness of cardiovascular diseaseinternal medicine and endocrinologypituitary adenoma sizepituitary disorderspulmonary disease

Identifiers

PMID41589173
PMCPMC12832130

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.