Identification and survival outcomes of a cohort of patients with cancer of unknown primary in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Cancer of unknown primary origin (CUP) is defined by the presence of pathologically identified metastatic disease without clinical or radiological evidence of a primary tumour. Our objective was to identify incident cases of CUP in Ontario, Canada, and determine the influence of histology and sites of metastases on overall survival (OS). MATERIAL AND METHODS: We used the Ontario Cancer Registry (OCR) and the Same-Day Surgery and Discharge Abstract Database (SDS/DAD) to identify patients diagnosed with CUP in Ontario between 1 January 2000, and 31 December 2005. Patient diagnostic information, including histology and survival data, was obtained from the OCR. We cross-validated CUP diagnosis and obtained additional information about metastasis through data linkage with the SDS/DAD database. OS was assessed using Cox regression models adjusting for histology and sites of metastases. RESULTS: We identified 3564 patients diagnosed with CUP. Patients without histologically confirmed disease (n = 1821) had a one-year OS of 10.9%, whereas patients with confirmed histology (n = 1743) had a one-year OS of 15.6%. The most common metastatic sites were in the respiratory or digestive systems (n = 1603), and the most common histology was adenocarcinoma (n = 939). Three-year survival rates were 3.5%, 5.3%, 41.6% and 3.6% among adenocarcinoma, unspecified carcinoma, squamous cell carcinoma and undifferentiated histology, respectively. Three-year survival rates were 40%, 2.4%, 8.0% and 4.6% among patients with metastases localised to lymph nodes, the respiratory or digestive systems, other specified sites, and unspecified sites, respectively. CONCLUSION: CUP patients in Ontario have a poor prognosis. Some subgroups may have better survival rates, such as patients with metastases localised to lymph nodes and patients with squamous cell histology.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".