International comparison of the impact of COVID on adherence with supportive care focused Choosing Wisely recommendations (CWR).
Bibliographic record
Abstract
28 Background: The reorganization of cancer care delivery during the COVID pandemic had the potential to catalyze improvement in CWR adherence by reducing provision of low value care to minimize in-person visits and mitigate potential issues with staff and resource shortages. We evaluated the impact of COVID on adherence with CWR for supportive care, relevant to colorectal and anal cancer patients with stage IV disease at Princess Margaret Cancer Centre (PM) in Canada and AC Camargo Cancer Center (AC) in Brazil. Methods: Eligible patients had a new patient consult 02/2020- 12/2020 (COVID) or the same period in 2019 (control). Performance on individual CWR in the 6 months following initial consultation was calculated as the proportion of eligible patients meeting the recommendation. Results: The PM and AC cohorts each consisted of 100 patients; demographic and disease characteristics of COVID and control cohorts were similar within each centre. Marginally fewer patients received surgery during COVID (PM: 38.3 vs 49.1%, p = 0.28; AC: 54.8 vs 55.2%, p = 0.97). At PM, more patients received radiation therapy during COVID (36.2 vs 24.5%, p = 0.21), whereas the opposite occurred at AC (7.1 vs 17.2%, p = 0.14). A higher proportion of both PM and AC patients treated during COVID died within 6 months of initial consult than in 2019 (PM: 10.6 vs 7.5%, p = 0.015; AC: 21.4 vs 8.6%, p = 0.029). Adherence to selected CWR is summarized below; whereby a higher proportion means higher concordance with CWR. Conclusions: There was low overall adherence to CWR across both centers with no significant changes to patterns of care for patients with stage IV disease during the COVID-19 pandemic.[Table: see text]
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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 | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".