The Diminishing Importance of Primary Site Identification in Cancer of Unknown Primary: A Canadian Single-Center Experience
Bibliographic record
Abstract
Background Cancer of unknown primary (CUP) describes patients with metastatic disease without an identified primary tumor site. Successful diagnosis and treatment of these patients remains difficult. Published guidelines on CUP have highlighted “favorable” subtype groups. We investigated a series of CUP patients to review adherence to guidelines, and identification of primary cancers or “favorable” subtypes. Methods Patients with histologically confirmed CUP at an academic institution from 2012 to 2018 were identified. Patient demographics, tumor presentation, diagnostic work-up and treatment information were retrospectively collected from electronic data records for descriptive analysis and compared to published clinical guidelines. The primary endpoint was the proportion of patients where the primary site was identified. Multivariable logistic regression models were used to identify factors associated with primary site identification. Kaplan-Meier survival curves were used to determine factors associated with poorer OS. Results Three hundred and five patients were included with a median follow-up time of 4.3 months. Primary tumor sites were identified in 109 patients (37.5%), which was most commonly lung cancer (33%). Statistical analyses did not identify any demographic or initial presentation factors associated with identifying the primary or not. More diagnostic tests did not increase the likelihood of primary site identification ( P=0.44 ). Patients with an identified primary did not have longer OS than other patients (median 5.2 months vs. 4.7 months, P=0.47 ). 57 patients (18.7%) who had a defined “favorable” subtype experienced superior OS (36.6 months vs. 3.8 months; P<0.0001 ). Further, patients with good prognostic status who followed published treatment guidelines had longer OS (17.6 months vs. 13.2 months; P=0.04 ). Conclusions CUP remains a difficult cancer to diagnose and treat. These results suggest identifying the primary has less impact than anticipated, but particular efforts to identify patients with “favorable” subtypes of CUP is important prognostically.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".