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Abstract PO-010: Changes in lung cancer treatment as a result of the COVID19 pandemic: A prospective observational study

2020· article· en· W3107233990 on OpenAlexaff
Suzanne Kazandjian, Nathaniel Bouganim, Arielle Elkrief

Bibliographic record

VenueClinical Cancer Research · 2020
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Diagnosis and Treatment
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsMedicineLung cancerCancerInternal medicineOncologyDiseaseChemotherapyObservational study

Abstract

fetched live from OpenAlex

Abstract Background: Patients with lung cancer are at high risk from COVID19. Efforts are ongoing to limit exposure of patients with lung cancer to the health care system. As a result, the COVID19 pandemic has drastically changed cancer care, but the extent and type of these changes are unknown. The goal of this study was to evaluate the changes in lung cancer treatment during the peak of the COVID19 pandemic. Methods: We prospectively assessed the cancer management plan of all patients seen in the thoracic oncology clinic at our center between March 2 and April 30, 2020. Inclusion criteria for this study were a diagnosis of either non-small cell lung cancer or small-cell lung cancer. Those who had a diagnosis of COVID19 were excluded from the study. Primary endpoints were to describe the extent of changes in the cancer treatment plan and qualify the types of changes observed. Results: A total of N=289 patients were evaluated between March 2 and April 30, 2020. N=14 patients were excluded due to presence of other tumor histology, and 2 patients were COVID19-positive. Among the 275 patients included, median age was 68 and 47% were male. Among the 238 patients (86.5%) with non-small cell lung cancer, 172 (62.5%) had advanced disease. Among the 37 patients (13.5%) with small-cell lung cancer, 11 (4%) had extensive disease. 211 were receiving active treatment (76.5%), with 35.1% on chemotherapy, 21.8% on oral agents, 31.8% on immune checkpoint inhibitors, and 11.4% on combination therapy. 121 (57%) of patients experienced at least one change in their lung cancer treatment plan as a direct result of the COVID19 pandemic, with 19 (9.0%) patients experiencing more than one change. The majority of changes encompassed delay or cessation of palliative treatment, N=48 (39.7%), N=18 (14.9%), respectively. Mean time to resumption of palliative treatment was 36 days, and 3% of patients stopped palliative treatment permanently as a direct result of the pandemic. Changes in dosing and schedule occurred in N=32 (26.4%), which included changing pembrolizumab to q 6 weeks or durvalumab to q 4 weeks. A minority of patients experienced delays in adjuvant chemotherapy administration (N=3 (2.5%)) with a mean delay of 42 days. Lastly, 6.6% of patients experienced deferrals or cancellations of surveillance scans or visits due to COVID19. Other changes included the decision not to pursue palliative chemotherapy. Conclusion: Our study demonstrated that a significant proportion (57%) of patients experienced changes in their lung cancer management plan as a direct result of the COVID19 pandemic. Given the preliminary findings that active cancer treatment is not associated with increased complications from COVID19, lung cancer treatments and surveillance visits should continue to proceed with caution, and oncology care providers should continue to carefully proceed with evidence-based care in lung cancer. Citation Format: Suzanne Kazandjian, Nathaniel Bouganim, Arielle Elkrief. Changes in lung cancer treatment as a result of the COVID19 pandemic: A prospective observational study [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-010.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.539
GPT teacher head0.601
Teacher spread0.062 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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