Development and validation of a Surgical Prioritization and Ranking Tool and Navigation Aid for Head and Neck Cancer (SPARTAN‐HN) in a scarce resource setting: Response to the COVID‐19 pandemic
Bibliographic record
Abstract
BACKGROUND: In the wake of the coronavirus disease 2019 (COVID-19) pandemic, access to surgical care for patients with head and neck cancer (HNC) is limited and unpredictable. Determining which patients should be prioritized is inherently subjective and difficult to assess. The authors have proposed an algorithm to fairly and consistently triage patients and mitigate the risk of adverse outcomes. METHODS: Two separate expert panels, a consensus panel (11 participants) and a validation panel (15 participants), were constructed among international HNC surgeons. Using a modified Delphi process and RAND Corporation/University of California at Los Angeles methodology with 4 consensus rounds and 2 meetings, groupings of high-priority, intermediate-priority, and low-priority indications for surgery were established and subdivided. A point-based scoring algorithm was developed, the Surgical Prioritization and Ranking Tool and Navigation Aid for Head and Neck Cancer (SPARTAN-HN). Agreement was measured during consensus and for algorithm scoring using the Krippendorff alpha. Rankings from the algorithm were compared with expert rankings of 12 case vignettes using the Spearman rank correlation coefficient. RESULTS: A total of 62 indications for surgical priority were rated. Weights for each indication ranged from -4 to +4 (scale range; -17 to 20). The response rate for the validation exercise was 100%. The SPARTAN-HN demonstrated excellent agreement and correlation with expert rankings (Krippendorff alpha, .91 [95% CI, 0.88-0.93]; and rho, 0.81 [95% CI, 0.45-0.95]). CONCLUSIONS: The SPARTAN-HN surgical prioritization algorithm consistently stratifies patients requiring HNC surgical care in the COVID-19 era. Formal evaluation and implementation are required. LAY SUMMARY: Many countries have enacted strict rules regarding the use of hospital resources during the coronavirus disease 2019 (COVID-19) pandemic. Facing delays in surgery, patients may experience worse functional outcomes, stage migration, and eventual inoperability. Treatment prioritization tools have shown benefit in helping to triage patients equitably with minimal provider cognitive burden. The current study sought to develop what to the authors' knowledge is the first cancer-specific surgical prioritization tool for use in the COVID-19 era, the Surgical Prioritization and Ranking Tool and Navigation Aid for Head and Neck Cancer (SPARTAN-HN). This algorithm consistently stratifies patients requiring head and neck cancer surgery in the COVID-19 era and provides evidence for the initial uptake of the SPARTAN-HN.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".