<scp>COVID</scp>‐19 pandemic and health care disparities in head and neck cancer: Scanning the horizon
Bibliographic record
Abstract
The COVID-19 pandemic has profoundly disrupted head and neck cancer (HNC) care delivery in ways that will likely persist long term. As we scan the horizon, this crisis has the potential to amplify preexisting racial/ethnic disparities for patients with HNC. Potential drivers of disparate HNC survival resulting from the pandemic include (a) differential access to telemedicine, timely diagnosis, and treatment; (b) implicit bias in initiatives to triage, prioritize, and schedule HNC-directed therapy; and (c) the marked changes in employment, health insurance, and dependent care. We present four strategies to mitigate these disparities: (a) collect detailed data on access to care by race/ethnicity, income, education, and community; (b) raise awareness of HNC disparities; (c) engage stakeholders in developing culturally appropriate solutions; and (d) ensure that surgical prioritization protocols minimize risk of racial/ethnic bias. Collectively, these measures address social determinants of health and the moral imperative to provide equitable, high-quality HNC care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.233 | 0.045 |
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".