The effect of the COVID-19 pandemic on the diagnosis and progression of oral cancer
Bibliographic record
Abstract
The COVID-19 pandemic placed a significant burden on healthcare resources, limiting care to emergent and essential services only. The objective of this study was to describe the effect of the COVID-19 pandemic on the diagnosis and progression of oral cancer lesions in Montreal, Canada. A retrospective analysis of health records was performed. Patients presenting for a new oncology consultation for an oral lesion suspicious for cancer between March 2018 and March 2022, within the Department of Oral and Maxillofacial Surgery of the McGill University Health Center, were included. Data was collected on sociodemographic characteristics, oral cancer risk behaviors of study participants, oral cancer delays, tumor characteristics, and clinical management. A total of 190 patients were included, 91 patients from the pre-pandemic period and 99 from the pandemic period. The demographic characteristics of the patients in the two periods were comparable. There was no significant difference in the patient, professional, or treatment delay between the two periods. There was a non-significant increase in pathologic tumor size during the pandemic, but the pathologic staging and postoperative outcomes were comparable to those of the pre-pandemic cohort. The results indicate that emergent care pathways for oral cancer treatment were efficiently maintained despite the pandemic shutdown of services.
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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".