Nesidioblastosis in Pregnancy: Navigating the Diagnostic and Therapeutic Challenges of a Rare Condition
Bibliographic record
Abstract
Nesidioblastosis, a non-neoplastic proliferation of the pancreatic islet cells of Langerhans, is a rare cause of endogenous hyperinsulinemic hypoglycemia. Although initially thought of as a congenital disease affecting pediatric patients, it is well known nowadays to affect adults as well. In addition, it is increasingly documented as a rare sequela of bariatric surgeries. Management options include medical and surgical therapies, with little known about the beneficial effects of both. Nesidioblastosis affecting pregnant patients is even rarer, with scarce literature known about the optimal treatment. Herein, we present a 22-year-old woman in the 20th week of gestation who experienced symptomatic episodes of severe life-threatening hypoglycemia. The management of this case required a multidisciplinary team approach to navigate the complexities of diagnosis and treatment. The complexity of this case was further heightened by the differential diagnosis, including conditions like insulinoma. The scarcity of literature on nesidioblastosis in pregnancy further complicated the case, underscoring the need for more research and case studies to guide clinical practice.
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 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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".