Impact of neuroradiologist second opinion on staging and management of head and neck cancer
Bibliographic record
Abstract
OBJECTIVE: Patients with head and neck cancer frequently present to academic tertiary referral centers with imaging studies that have been performed and interpreted elsewhere. At our institution, these outside head and neck imaging studies undergo formal second opinion reporting by a fellowship-trained academic neuroradiologist with expertise in head and neck imaging. The purpose of this study was to determine the impact of this practice on cancer staging and patient management. METHODS: Our institutional review board approved the retrospective review of randomized original and second opinion reports for 94 consecutive cases of biopsy proven or clinically suspected head and neck cancer in calendar year 2010. Discrepancy rates for staging and recommended patient management were calculated and, for the 32% (30/94) of cases that subsequently went to surgery, the accuracies of the reports were determined relative to the pathologic staging gold standard. RESULTS: Following neuroradiologist second opinion review, the cancer stage changed in 56% (53/94) of cases and the recommended management changed in 38% (36/94) of patients with head and neck cancer. When compared to the pathologic staging gold standard, the second opinion was correct 93% (28/30) of the time. CONCLUSION: In a majority of patients with head and neck cancer, neuroradiologist second opinion review of their outside imaging studies resulted in an accurate change in their cancer stage and this frequently led to a change in their management plan.
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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 | 0.081 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".