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Comparison of Use of Neoadjuvant Systemic Treatment for Breast Cancer and Short-term Outcomes Before vs During the COVID-19 Era in Ontario, Canada

2022· article· en· W4289443186 on OpenAlexaffabout
Steven Habbous, Xiaochen Tai, Jaclyn Beca, Jessica Arias, Michael J. Raphael, Ambica Parmar, Andrea Crespo, Matthew C. Cheung, Andrea Eisen, Antoine Eskander, Simron Singh, Maureen Trudeau, Scott Gavura, Wei Fang Dai, Jonathan C. Irish, Monika K. Krzyzanowska, Lauren Lapointe‐Shaw, Rohini Naipaul, Stuart Peacock, Lyndee Yeung, Leta Forbes, Kelvin Chan

Bibliographic record

VenueJAMA Network Open · 2022
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsLakeridge HealthPrincess Margaret Cancer CentreRegional Municipality of DurhamUniversity of TorontoHealth Sciences CentreCanadian Centre for Applied Research in Cancer ControlSunnybrook Health Science CentreCancer Care Ontario
Fundersnot available
KeywordsTerm (time)Coronavirus disease 2019 (COVID-19)MedicineBreast cancerSystemic therapyOncologyCancerNeoadjuvant therapyInternal medicineIntensive care medicineDemographyDiseaseSociology

Abstract

fetched live from OpenAlex

Importance: In response to an increase in COVID-19 infection rates in Ontario, several systemic treatment (ST) regimens delivered in the adjuvant setting for breast cancer were temporarily permitted for neoadjuvant-intent to defer nonurgent breast cancer surgical procedures. Objective: To examine the use and compare short-term outcomes of neoadjuvant-intent vs adjuvant ST in the COVID-19 era compared with the pre-COVID-19 era. Design, Setting, and Participants: This was a retrospective population-based cohort study in Ontario, Canada. Patients with cancer starting selected ST regimens in the COVID-19 era (March 11, 2020, to September 30, 2020) were compared to those in the pre-COVID-19 era (March 11, 2019, to March 10, 2020). Patients were diagnosed with breast cancer within 6 months of starting systemic therapy. Main Outcomes and Measures: Estimates were calculated for the use of neoadjuvant vs adjuvant ST, the likelihood of receiving a surgical procedure, the rate of emergency department visits, hospital admissions, COVID-19 infections, and all-cause mortality between treatment groups over time. Results: Among a total of 10 920 patients included, 7990 (73.2%) started treatment in the pre-COVID-19 era and 7344 (67.3%) received adjuvant ST; the mean (SD) age was 61.6 (13.1) years. Neoadjuvant-intent ST was more common in the COVID-19 era (1404 of 2930 patients [47.9%]) than the pre-COVID-19 era (2172 of 7990 patients [27.2%]), with an odds ratio of 2.46 (95% CI, 2.26-2.69; P < .001). This trend was consistent across a range of ST regimens, but differed according to patient age and geography. The likelihood of receiving surgery following neoadjuvant-intent chemotherapy was similar in the COVID-19 era compared with the pre-COVID-19 era (log-rank P = .06). However, patients with breast cancer receiving neoadjuvant-intent hormonal therapy were significantly more likely to receive surgery in the COVID-19 era (log-rank P < .001). After adjustment, there were no significant changes in the rate of emergency department visits over time between patients receiving neoadjuvant ST, adjuvant ST, or ST only during the ST treatment period or postoperative period. Hospital admissions decreased in the COVID-19 era for patients who received neoadjuvant ST compared with adjuvant ST or ST alone (P for interaction = .01 for both) in either setting. Conclusions and Relevance: In this cohort study, patients were more likely to start neoadjuvant ST in the COVID-19 era, which varied across the province and by indication. There was limited evidence to suggest any substantial impact on short-term outcomes.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.016
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.117
GPT teacher head0.404
Teacher spread0.287 · 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 teacher head, 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

Citations25
Published2022
Admission routes2
Has abstractyes

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