Risk of cancer in acromegaly patients: An updated meta-analysis and systematic review
Bibliographic record
Abstract
The incidence of cancer in acromegaly patients may be higher than that in the general population, although this has not been fully elucidated yet. This study analyzed the risk of various important types of cancer in acromegaly patients. The study was registered in INPLASY (registration number: INPLASY202340037). The PubMed, Web of Science, and EMBASE databases were searched for studies based on strict inclusion and exclusion criteria, from the time of database inception up to June 30, 2022. All observational studies of acromegaly patients with cancer were included, without language restrictions. We used the Newcastle-Ottawa scale (NOS) checklist to assess the quality of evidence. A meta-analysis revealed the relationship between acromegaly and cancer using the standardized incidence rates (SIRs) and 95% confidence intervals (CIs) retrieved from the included studies. Nineteen studies were included and analyzed. The overall incidence of cancer (SIR = 1.45, 95%CI = 1.20-1.75), as well as that of thyroid (SIR = 6.96, 95%CI = 2.51-19.33), colorectal and anal (SIR = 1.95, 95%CI = 1.32-2.87), brain and central nervous system (SIR = 6.14, 95%CI = 2.73-13.84), gastric (SIR = 3.09, 95%CI = 1.47-6.50), urinary (SIR = 2.66, 95%CI = 1.88-3.76), hematological (SIR = 1.89, 95%CI = 1.17-3.06), pancreatic and small intestine (SIR = 2.59, 95%CI = 1.58-4.24), and connective tissue (SIR = 3.15, 95%CI = 1.18-8.36) cancers, was higher among patients with acromegaly than among the general population. No association between acromegaly and hepatobiliary, respiratory, reproductive, skin, breast, or prostate cancer was observed. This study demonstrated that acromegaly patients have a modestly increased chance of cancer as compared to the general population. Risk factors for cancer need to be further explored to monitor patients with acromegaly at a high risk for cancer more carefully.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.035 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".