The Clinicopathological Spectrum of Acromegaly
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".