Mini-review: Current challenges in the treatment of developmentally diverse neuroendocrine like tumors: Comparison of bronchial carcinoids and neuroblastoma
Bibliographic record
Abstract
Bronchial carcinoids (BC) derive from the pulmonary neuroendocrine cell system while neuroblastoma (NB) derives from the neural crest and represents the peripheral nervous system. Nevertheless, their production of serotonin and catecholamines, respectively, permits comparison as neuroendocrine tumors (NETs). BC are most often diagnosed in adults accounting for 1–5% of all invasive lung malignancies in the adults. NB, primarily a pediatric cancer, accounts for 7-8% of all childhood cancers with the highest incidence in children younger than 5 years, and frequent metastasis to liver, skin, bone, and brain. The major standard treatments for BC is surgery followed by endocrine therapy and chemotherapies. NB patients on the other hand receive treatments according to presenting stage with chemotherapies dominant and now newer immunotherapies at more advanced stages. In general BC are more indolent but when less differentiated can be aggressive portending a poorer outcome. NB in contrast often present at an advanced stage and if high stage the overall survival approaches 25% despite extensive and other therapies. Early stage diagnosis still remains a challenge since symptomology depends on their neuroendocrine manifestation. Here we summarize the current knowledge and challenges in the management of BC and NB.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".