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Record W2985349631 · doi:10.3390/jcm8111962

The Clinicopathological Spectrum of Acromegaly

2019· review· en· W2985349631 on OpenAlexaff
Amit Akirov, L. Sylvia, Lama Amer, Ilan Shimon, Shereen Ezzat

Bibliographic record

VenueJournal of Clinical Medicine · 2019
Typereview
Languageen
FieldMedicine
TopicPituitary Gland Disorders and Treatments
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsAcromegalyMedicinePituitary tumorsSomatotropic cellGrowth hormonePathologicalPathologyInternal medicineHormone

Abstract

fetched live from OpenAlex

BACKGROUND: Acromegaly results from a persistent excess in growth hormone with clinical features that may be subtle or severe. The most common cause of acromegaly is a pituitary tumor that causes excessive production of growth hormone (GH), and rare cases are due to an excess of the GH-releasing hormone (GHRH) or the ectopic production of GH. OBJECTIVE: Discuss the different diseases that present with manifestations of GH excess and clinical acromegaly, emphasizing the distinct clinical and radiological characteristics of the different pathological entities. METHODS: We performed a narrative review of the published clinicopathological information about acromegaly. An English-language search for relevant studies was conducted on PubMed from inception to 1 August 2019. The reference lists of relevant studies were also reviewed. RESULTS: Pituitary tumors that cause GH excess have several variants, including pure somatotroph tumors that can be densely or sparsely granulated, or plurihormonal tumors that include mammosomatotroph, mixed somatotroph-lactotroph tumors and mature plurihomonal Pit1-lineage tumors, acidophil stem cell tumors and poorly-differentiated Pit1-lineage tumors. Each tumor type has a distinct pathophysiology, resulting in variations in clinical manifestations, imaging and responses to therapies. CONCLUSION: Detailed clinicopathological information will be useful in the era of precision medicine, in which physicians tailor the correct treatment modality to each patient.

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)0.005
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.985
Threshold uncertainty score0.779

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0070.002
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.256
GPT teacher head0.532
Teacher spread0.276 · 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 designOther design
Domainnot available
GenreReview

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

Citations72
Published2019
Admission routes1
Has abstractyes

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