Head and Neck Cancer Patient Population, Management, and Oncologic Outcomes from the COVID-19 Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic precipitated drastic changes in cancer care. Its impact on the U.S. head and neck cancer population has yet to be fully understood. This study aims to understand the impact of pandemic-related changes on the head and neck cancer population. An observational study of head and neck cancer patients at a single institution during the spring of 2020 and 2019 was performed. Clinical characteristics and survival outcomes were analyzed. In 2020, 54 head and neck cancer patients were evaluated in the department of radiation oncology vs. 74 patients seen in 2019; 42% of the patients were female in 2019 versus 24% in 2020 (p = 0.036). The median follow-up time was 19.4 and 31 months for 2020 and 2019, respectively. After adjusting for stage, the relapse-free survival probability at 6 and 12 months was 79% and 69% in 2020 vs. 96% and 89% in 2019, respectively (p = 0.036). There was no significant difference in the overall survival, with 94% and 89% in 2020 and 2019, respectively (p = 0.61). Twenty-one percent of patients received induction chemotherapy in 2020 versus 5% in 2019 (p = 0.011); significantly more treatment incompletions occurred in 2020, 9% vs. 0% in 2019 (p = 0.012). Moreover, the stage-adjusted RFS differed between cohorts, suggesting head and neck cancer patients seen during the initial wave of COVID-19 may experience worse oncologic outcomes.
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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".