Metachronous and Synchronous Multiple Primary Carcinomas in an Elderly
Bibliographic record
Abstract
The prognosis of most cancers has been improved in recent years due to the increased survival of cancer patients, the prolonged lifespan of the general population, and better diagnostic and surgical approaches. Subsequently, the number of patients with multiple primary carcinomas (MPCs) has become greater. In this report, we describe a unique case of a 71-year-old man with five metachronous and synchronous primary malignant tumors. The patient was first diagnosed with synchronous, left posterior tonsillar pillar squamous cell carcinoma (SCC) and right lingual surface of epiglottis SCC in June 2016. Two years later, he presented with three other primary carcinomas within a 3-month time span: right lower lobe lung SCC, right tongue invasive SCC and hepatocellular carcinoma (HCC), consecutively. Investigations revealed no metastases of the primary neoplasms. As the population of older adults with cancer and multimorbidity grows, the therapeutic options usually become limited. On the other hand, understanding the effect of multimorbidity on the care of patients with cancer and developing therapeutic interventions for these elderly patients would be crucial for geriatric care. J Med Cases. 2019;10(1):8-13 doi: https://doi.org/10.14740/jmc3172
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".