Impact of COVID-19 pandemic on neuropathology service: Experience at one Canadian center
Bibliographic record
Abstract
The COVID-19 pandemic has had a significant impact on medical services. Many countries postponed nonemergent procedures to preserve hospital resources for the unprecedented situation. Surgical backlogs caused by the COVID-19 pandemic have been evaluated by different groups. However, the impact of this pandemic on pathology and specifically neuropathology (NP) services has received limited attention. In this study, we reviewed all NP reports of the London Health Sciences Centre from January 2018 (2 years before the pandemic declaration) until the end of the year 2021. Demographic information and pathology details were collected. For tumors, site, histopathology types, and WHO grading were analyzed. In nontumoral specimens, pathological diagnoses were compared in pre- and postpandemic time. The total number of NP samples reached its lowest in April 2020, corresponding to the first Ontario provincial lockdown, and fluctuated throughout the studied period. Among the different types of NP surgical specimens, muscle and epilepsy-related specimens showed a more significant reduction, compared to neoplastic specimens. In 2020, the proportion of tumor specimens from patients older than 40 years of age increased. Similarly, the proportion of high-grade glioma and brain metastasis diagnoses also increased. Lastly, we observed a marked increase in biopsies for temporal arteritis and other inflammatory lesions.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".