Stay at home: implementation and impact of virtualising cancer genetic services during COVID-19
Bibliographic record
Abstract
The COVID-19 pandemic has led to the rapid adoption of virtual clinic processes and healthcare delivery. Herein, we examine the impact of virtualising genetics services at Canada's largest cancer centre. A retrospective review was conducted to evaluate relevant metrics during the 12 weeks prior to and during virtual care, including referral and clinic volumes, patient wait times and genetic testing uptake. The number of appointments and new patients seen were maintained during virtual care. Likewise, there was a significant increase in the number of patients offered testing during virtual care who did not provide a blood sample (176/180 (97.7%) vs 180/243 (74.1%); p<0.001), and a longer median time from the date of pretest genetic counselling to the date a sample was given (0 vs 11 days; p<0.001). Referral volumes significantly decreased during virtual care (35 vs 22; p<0.001), which was accompanied by a decreased median wait time for first appointment (55 days vs 30 days; p<0.001). The rapid virtualisation of cancer genetic services allowed the genetics clinic to navigate the COVID-19 pandemic without compromising clinical volumes or access to genetic testing. There was a decrease in referral volumes and uptake of genetic testing, which may be attributable to pandemic-related clinical restrictions.
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".