Melbourne colorectal collaboration: a multicentre review of the impact of <scp>COVID</scp>‐19 on colorectal cancer in Melbourne, Australia
Bibliographic record
Abstract
BACKGROUND: As coronavirus (COVID-19) cases continue to rise, healthcare workers have been working overtime to ensure that all patients receive care in a timely manner. Our study aims to identify the impact and outcomes of COVID-19 on colorectal cancers presentations across the five major colorectal units in Melbourne, Australia. METHODS: This is a retrospective study from a prospectively collected database from the binational colorectal cancer audit (BCCA) registry, as well as inpatient records. All patients with colorectal cancer between Pre-COVID-19 period (1 July 2018-2030 June 2019) and COVID-19 period (1 July 2020-2030 June 2021) were compared. Benign pathology and other cancer types were excluded. RESULTS: A total of 1609 patients were included in the study (700 Pre-COVID-19 period, 906 COVID-19 period). During COVID-19 period, there was a higher proportion of emergency surgery (28.1% vs. 19.8%; P < 0.001), a higher nodal (P = 0.024) and metastatic stage (P = 0.018) at presentation, but no increase in the rate of return to operating theatres (P = 0.240), inpatient death (P = 0.019) or 30-day readmission (P = 0.000). There was also no difference in the post-operative surgical complications (P = 0.118). Utility of neoadjuvant therapy did not increase during the pandemic (P = 0.613). CONCLUSION: The heightened measures in the healthcare system ensured CRC patients still received their surgery in a timely fashion. With the current rise in the new strain of COVID-19 (Omicron), we have to continue to come up with new strategies to provide timely access to CRC care.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".