Impact of the Early Phase of the COVID-19 Pandemic on the Quality of Care for Colorectal and Anal Cancers at Comprehensive Cancer Centers on Two Continents
Bibliographic record
Abstract
PURPOSE The early phase of the COVID-19 pandemic affected cancer care globally. Evaluating the impact of the pandemic on the quality of cancer care delivery is crucial for understanding how changes in care delivery may influence outcomes. Our study compared care delivered during the early phase of the pandemic with the same period in the previous year at two institutions across continents (Princess Margaret Cancer Center [PM] in Canada and A.C. Camargo Cancer Center [AC] in Brazil). METHODS Patients newly diagnosed with colorectal or anal cancer between February and December 2019 and the same period in 2020 were analyzed. Sociodemographic and clinical characteristics and performance of individual indicators within and between centers and between the peri–COVID-19 and control cohorts were tested using Cohen's h test to assess the standardized differences between the two groups. RESULTS Among 925 patients, distinct effects of the early COVID-19 pandemic on oncology services were observed. AC experienced a 50% reduction in patient consultations (98 v 197) versus a 12.5% reduction at PM (294 v 336). Similarly, AC experienced a higher proportion of stage IV disease presentations (42.9% v 29.9%; P = .015) and an increase in treatment delay (61.9% v 9.7%; P < .001) compared with prepandemic. At PM, a 10% increase in treatment interruption (32.4% v 22.3%; P < .001) and a higher rate of discontinuation of radiotherapy (9.4% v 1.1%; P < .001) were observed during the pandemic. Postsurgical readmission rates increased in both AC (20.9% v 2.6%; P < .001) and PM (10.5% v 3.6%; P < .01). CONCLUSION The early phase of the COVID-19 pandemic affected the quality of care delivery for colorectal and anal cancers at both centers. However, the magnitude of this impact was greater in Brazil.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".