Abstract PO-026: Minimize impact of pandemic on radiation oncology department: Experience from a moderate-sized regional cancer program in the battle against COVID-19 virus
Bibliographic record
Abstract
Abstract Introduction: The World Health Organization (WHO) declared COVID-19 virus as pandemic on March 12, 2020. Now it has infected more than 5 million people in 188 countries and caused more than 340,000 deaths. The purpose of this paper is to share the experience of our radiation oncology department in a moderate-sized regional cancer center (1,600 new consults for radiation per year) in the battle against COVID-19, including the safety measures taken and the lessons learned. Methods: Our institution is located in the neighbor city of one of the largest COVID-19 epicenters in the USA. We have taken precautionary measures gradually to continue our practice in radiation oncology in order to reduce impact on vulnerable cancer patients. These include reducing the number of entrance doors for both staff and patients; restricted visitor policy; mandatory screening questionnaires; social distancing in waiting rooms; self-quarantine of staff with travel history or symptoms similar to COVID-19; most responsible physicians reviewing every case to prioritize or defer consultation, investigation, or treatment; telemedicine for most consultation and follow-up visits; universal COVID-19 swabbing test for all symptomatic and asymptomatic cancer patients before starting treatment planning or radiotherapy; full personal protective equipment (PPE) for staff doing CT simulation or delivering treatment; mandatory face mask for everyone in the building; keeping 2/3 of all radiation oncologists (RO) and dosimetrists working from home on a roster schedule; and discouraging handling physical paper charts and documents in a completely paperless working environment. Results: We saw 267 new consults in the 10 weeks between March 16 and May 24, 2020, vs. 274 in the same period last year. There is no significant difference in average consults per RO, 44.5 (30-60) vs. 45.7 (24-67), p=0.799 (Student’s t-test), or wait time within provincial target of 2 weeks, 93.5% vs 97%, p=0.074. We performed 193 swabbing tests for 183 patients, with 10 patients bein.g swabbed twice. Most were asymptomatic (144), with 49 symptomatic. Only 0.52% tested positive (1 asymptomatic case), lower than many other cancer institutions reported in the literature, and there were no cases among staff. During the same 10 weeks, confirmed cases in our community and the province increased from 0 to 912 (6.05% positive tests) and from 142 to 25,904 (4.18% positive tests), with 63 and 2,102 deaths, respectively. Conclusions: Due to restrictions to test asymptomatic patients and to use PPE, the COVID-19 testing rate is far from reaching the provincial target and the new cases and deaths are more than originally predicted. However, our department was not heavily affected due to the diligent team effort ahead of policy changes in the province. It is possible for frontline health care teams to minimize the risk of cancer patients getting COVID-19 and avoid treatment interruptions by planning safety measures early, even before the first case in the community and before formal provincial guidelines become available. Citation Format: Ming Pan, Khalid Hirmiz, Junaid Yousuf, Kitty Huang, Colvin Springer, Ken Schneider, Laura D’Alimonte. Minimize impact of pandemic on radiation oncology department: Experience from a moderate-sized regional cancer program in the battle against COVID-19 virus [abstract]. In: Proceedings of the AACR Virtual Meeting: COVID-19 and Cancer; 2020 Jul 20-22. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(18_Suppl):Abstract nr PO-026.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".