Like a Complete Unknown: An Audit of the Quality of the Referrals to the Cancer of Unknown Primary Clinic at a Tertiary Care Centre
Bibliographic record
Abstract
BACKGROUND: Carcinoma of Unknown Primary (CUP) constitutes approximately 3% of all advanced cancer cases globally, posing a distinct and complex medical challenge due to its metastatic nature, with no identifiable primary tumour site despite comprehensive investigations. AIM: This study aimed to assess the quality of referrals to the Cancer of Unknown Primary Clinic at the Princess Margaret Cancer Centre (PMCC) by conducting a retrospective audit of initial referrals between January 2022 and March 2023. METHODS: The adequacy of referrals was evaluated based on adherence to NICE guidelines, focusing on essential diagnostic investigations such as comprehensive history, physical examination, CT scans, and pathological assessment with immunohistochemistry. Our cohort consisted of 97 patients with a median age of 66 years. RESULTS: The results indicated that only 55% of referrals met the criteria for adequacy, with significant deficiencies in computed tomography (CT) scans and immunohistochemistry (IHC). Notably, the adequacy of referrals varied by specialty, with the lowest rates in emergency medicine and family medicine, and the highest rates in medical oncology, gastroenterology, and neurosurgery. CONCLUSIONS: These findings underscore the need for improved standardization and education to enhance referral quality, ensuring that patients with CUP receive appropriate and timely care. This study marks the initial phase of the Knowledge-to-Action cycle, highlighting areas for quality improvement in the referral process to the CUP clinic.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".