Utilization of a rapid diagnostic centre during the COVID-19 pandemic reduced diagnostic delays in breast cancer
Bibliographic record
Abstract
BACKGROUND: Access to breast imaging was restricted during the first wave of the COVID-19 pandemic. We assessed the impact of healthcare restrictions on the Gattuso Rapid Diagnostic Centre (GRDC) at the Princess Margaret Cancer Centre. METHODS: A retrospective review of patients seen at the GRDC between March 12 - August 31, 2020 and the corresponding period from 2019 was performed. RESULTS: There was an 18.6% decrease in patients seen at the GRDC (n = 429 in 2020 vs. 527 in 2019). Time from the first abnormal breast image to diagnosis was significantly shorter (17.4 days [IQR 13.0-21.8] in 2020 vs. 25.9 days [21.0-30.8] in 2019; p = 0.020) with no appreciable difference in time from diagnosis to consult or from consult to surgery. CONCLUSION: The GRDC enabled patients with concerning breast symptoms to access breast imaging, which helped to ensure timely treatment during the first wave of the pandemic.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".