Correlation between imaging-detected and pathological extranodal extension in a randomised trial in Human Papillomavirus-positive oropharyngeal cancer
Bibliographic record
Abstract
BACKGROUND: Imaging-detected and pathological extranodal extension (iENE, pENE) negatively impact prognosis in Human Papillomavirus (HPV)-positive oropharyngeal cancer (OPSCC), as reflected in future TNM staging updates. Correlation between iENE and pENE in HPV-positive OPSCC is currently unknown yet is vital to determine how iENE should be used to influence treatment decisions. METHODS: PATHOS is a trial of de-intensified adjuvant treatment after transoral surgery for HPV-positive OPSCC. 291 consecutively recruited patients undergoing surgery at three UK centres were included. Pre-operative cross-sectional imaging (CT and/or MRI) was independently scored for iENE by 2 expert radiologists; pENE was scored by 2 expert pathologists. RESULTS: Inter-rater agreement for iENE was fair in round 1 (Gwet's AC: 0.34 (95%CI:0.26-0.41)) but improved to very good after second review (Gwet's AC: 0.88 (95%CI:0.85-0.93), Agreement: 0.91 (95%CI:0.87-0.94)). Sensitivity of iENE for predicting pENE was relatively low (at best: 56.4% (95%CI:42.3-69.7) and specificity was high (at worst: 70.9% (95%CI:65.0-76.3)). Excluding cases with suboptimal image quality and recent core biopsy produced modest improvements in sensitivity (up to 59.4% (95%CI:40.6-76.3)) and specificity (up to 87.8% (95%CI:80.4-93.2)). DISCUSSION: The high specificity could help select iENE-negative patients for surgery, but higher sensitivity is required before excluding surgery based solely on iENE positivity.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".