Prioritization of head and neck cancer patient care during the COVID-19 pandemic: A retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic placed considerable strain on the healthcare system, leading to the re-allocation of resources and implementation of new practice guidelines. The objective of this study is to assess the impact of COVID-19 guideline modifications on head and neck cancer (HNC) care at two tertiary care centers in Canada. METHODS: A retrospective cohort study was conducted. HNC patients seen at two tertiary care centers before and after the onset of the COVID-19 pandemic (pre-pandemic: July 1st, 2019, to February 29th, 2020; pandemic: March 1st, 2020, to October 31st, 2020) were included. The pre-pandemic and pandemic cohorts were compared according to patient and tumor characteristics, duration of HNC workup, and treatment type and duration. Mean differences in cancer care wait times, including time to diagnosis, tumor board, and treatment as well as total treatment package time and postoperative hospital stay were compared between cohorts. Univariate and multivariate analyses were used to compare characteristics and outcomes between cohorts. RESULTS: Pre-pandemic (n = 132) and pandemic (n = 133) patients did not differ significantly in sex, age, habits, or tumor characteristics. The percentage of patients who received surgery only, chemo/radiotherapy (CXRT) only, and surgery plus adjuvant CXRT did not differ significantly between cohorts. Pandemic patients experienced a significant time reduction compared to pre-pandemic patients with regards to the date first seen by a HNC service until start of treatment ([Formula: see text] = 48.7 and 76.6 days respectively; p = .0001), the date first seen by a HNC service until first presentation at tumor board ([Formula: see text] = 25.1 and 38 days respectively; p = .001), mean total package time for patients who received surgery only ([Formula: see text] = 3.7 and 9.0 days respectively; p = .017), and mean total package time for patients who received surgery plus adjuvant CXRT ([Formula: see text] = 80.2 and 112.7 days respectively; p = .035). CONCLUSION: The time to treatment was significantly reduced during the COVID-19 pandemic as compared to pre-pandemic. This transparent model of patient-centered operative-room prioritization can serve as a model for improving resource allocation and efficiency of HNC care during emergency and non-emergency scenarios.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".