The impact of COVID-19 on the practice of Oral and Maxillofacial Pathology in the United States and Canada
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has significantly disrupted the delivery of healthcare, including oral healthcare services. The restrictions imposed for mitigating spread of the virus forced dental practitioners to adopt significant changes in their workflow pattern. The aim of this study was to investigate the impact of the pandemic on the practice of oral and maxillofacial pathology in two countries in regard to educational activities, and clinical and diagnostic pathology services. MATERIAL AND METHODS: An online questionnaire was distributed to oral and maxillofacial pathologists in the United States and Canada. The survey was designed by combining dichotomous, multiple choice, and Likert response scale questions. Statistical analysis of the collected data was performed with SPSS software. RESULTS: Most pathologists, at the time of survey completion, were teaching synchronously, primarily with case-based learning and live lectures. During lockdown, 52.4% and 50.0% of those with trainees expected their residents to show up for clinic- and laboratory-related procedures respectively. The pathologists were most concerned for their residents' inadequate clinical exposure, future placement, and face-to-face teaching time. About 89.0% pathologists were able to provide emergent care, with 82.4% and 23.5% having performed telehealth consultations and oral biopsy procedures, respectively. During the lockdown, the pathology laboratories for 90.9% of participants received biopsy specimens that predominantly comprised of potentially malignant or malignant lesions. However, a reduction in the number of biopsy submissions was reported. CONCLUSIONS: Given the challenges of the pandemic, oral and maxillofacial pathologists in the United States and Canada successfully continued their pursuits in education, clinical care, and diagnostic pathology services.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".