Management delays in patients with squamous cell cancer of neck node(s) and unknown primary site: A retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: We aim to characterize the workup received by and identify any delays to diagnosis or treatment in patients referred to a tertiary cancer centre with the diagnosis of squamous cell carcinoma in neck node(s) and no identifiable primary (SCCNIP). METHODS: Over 1 year, 68 patients were initially referred to the Head and Neck clinic with a label of "primary unknown". After extensive workup, 29 of the 68 patients were found to have pathologically confirmed SCCNIP. For these 29 patients, imaging tests, biopsies, examinations and times to treatment were reviewed and compared to 145 patients referred for known primaries. RESULTS: In 21/29 (72%) patients, ultrasound was ordered prior to biopsy or referral. After referral, the first imaging test used was CT neck in 28 patients and PET/CT in 1 patient. Median time from referral to primary identification (n = 23) or workup completion (n = 6) were 16 (range: 0-48) and 36 (17-82) days respectively. Median time from referral to treatment was 55 (27-90; n = 26) days and was longer than those referred for known primaries (48 days; 20-162; p < 0.001). Across all patients, median time between first diagnostic imaging test and pathologic diagnosis were 20.5 and -8.0 days (p < 0.0001) in patients receiving ultrasound and CT, respectively. CONCLUSIONS: In our cohort, delays to management were linked to community use of ultrasound and scheduling of both CT and PET/CT after thorough head and neck examination in patients with SCCNIP.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".