Small cell lung carcinoma metastatic to the stomach: Commonly overlooked, limited treatment options
Bibliographic record
Abstract
Small cell lung carcinoma metastatic to the stomach, whether synchronous or metachronous, is a rare phenomenon accounting for < 0.5% of lung cancers. Hence it can be overlooked by clinicians resulting in delayed diagnosis. This manuscript comments on Yang et al ’s article which reported 3 such cases. The main diagnostic features are based on routine morphology comprised of small cells with hyperchromatic nuclei, scant cytoplasm, brisk mitoses and necrosis. This can be supplemented by immunohistochemistry demonstrating positivity for cytokeratin, thyroid transcription factor-1 and neuroendocrine markers as well as a high Ki-67 labelling index. Imaging modalities such as positron emission tomography/contrast computed tomography help to confirm lung origin and rule out the possibility of extra-pulmonary small cell carcinoma. The predominant mechanism of spread is most likely hematogeneous. Prognosis is generally poor since this represents stage 4 disease but survival can be improved by chemo/radiotherapy and palliative surgery in select cases. Though outcomes have not changed much in the last several decades, the recent Food and Drug Administration approval of immune checkpoint inhibitors was a significant milestone as was the delineation of small cell lung carcinoma molecular subtypes. Liquid biopsies are increasingly being used for biomarker studies in clinical trials to assess treatment response and prognosis.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| 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".