Perceptions of cancer of unknown primary site: a national survey of Australian medical oncologists
Bibliographic record
Abstract
BACKGROUND: Despite being the sixth most common cause of cancer death in Australia, cancer of unknown primary (CUP) site remains poorly understood. AIMS: To describe practices relating to the diagnosis, investigation, classification, communication and management of CUP among medical oncologists. METHODS: We invited all members of the Medical Oncology Group of Australia to participate in a national, anonymous online survey about CUP. The survey collected data regarding diagnosis acceptance, diagnostic tests, treatment protocols and communication practices around the diagnosis of CUP. RESULTS: Three hundred and two oncologists were invited and 86 (28%) completed the survey. Eighty (93%) respondents were directly involved in the assessment of patients with CUP. Eighty-five (99%) respondents were prepared to make a diagnosis of CUP if, after appropriate diagnostic tests, the primary location could not be ascertained. Eighty-three percent would assign a primary site to obtain Pharmaceutical Benefits Schedule funding of medical therapy. Sixty-two percent did not have a specific treatment protocol designed for CUP. The majority of oncologists used serum tumour markers and computed tomography scans in the initial work-up, while 43% indicated they would use a positron emission tomography scan in the majority of cases. The majority would arrange mammography in female patients. Thematic analysis of responses to open-ended questions about how CUP is described identified little consistency in the language being used. CONCLUSION: The approach to diagnosis, investigation and management of CUP by medical oncologists in Australia is variable. Many preferred to estimate the primary site and treat accordingly. Pharmaceutical Benefits Schedule restrictions may encourage the practice of 'best guessing'.
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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 | 0.004 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".