Rapid implementation of an evidence-based remote triaging system for assessment of suspected head and neck cancer referrals and patients on follow up after treatment during the COVID-19 pandemic: A model for international collaboration
Bibliographic record
Abstract
Background Outpatient telemedicine consultations are being adopted to triage patients for head and neck cancer. However, there is currently no established structure to frame this consultation. Methods For suspected cancer referrals, we adapted the Head and Neck Cancer Risk Calculator (HaNC-RC)-V.2, generated from 10,244 referrals with the following diagnostic efficacy metrics: 85% sensitivity, 98.6% negative predictive value and area under the curve of 0.89. For follow up patients, a symptom inventory generated from 5,123 follow-up consultations was used. A customised Excel Data Tool was created, trialled across professional groups and made freely available for download at www.entintegrate.co.uk/entuk2wwtt , alongside a user guide, protocol and registration link for the project. Stakeholder support was obtained from national bodies. Results No remote consultations were refused by patients. Preliminary data from 511 triaging episodes at 13 centres show that 77.1% of patients were discharged directly or have had their appointments deferred. Discussion Significant reduction in footfall can be achieved using a structured triaging system. Further refinement of HaNC-RC V.2 is feasible and the authors welcome international collaboration.
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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 | 0.021 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".