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Record W4389318542 · doi:10.1590/0102-311xen057423

Uncovering the impact of COVID-19 on the place of death of cancer patients in South America

2023· article· en· W4389318542 on OpenAlexafffund
Doris Durán, Renzo Calderón-Anyosa, Belinda Nicolau, Jay S. Kaufman

Bibliographic record

VenueCadernos de Saúde Pública · 2023
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersFonds de recherche du QuébecRéseau de cancérologie RossyMcGill University
KeywordsDeath certificatePandemicLatin AmericansMedicinePlace of deathDemographyCoronavirus disease 2019 (COVID-19)Mortality rateCancerCause of deathEnvironmental healthGerontologyPalliative careDiseaseNursingSurgeryPathology

Abstract

fetched live from OpenAlex

The COVID-19 pandemic has significantly impacted healthcare systems worldwide, especially on the management of chronic diseases such as cancer. This study explores the effects of COVID-19 on cancer mortality trends in Brazil, Chile, and Peru. The monthly age-standardized mortality rates in different places of death (hospital/clinic or home) were estimated using vital statistics and death certificate databases. An interrupted time series analysis was performed for each country, using the date of lockdown implementation as the intervention point. Overall cancer mortality rates reduced after the implementation of pandemic restrictions, with a significant decrease in Brazil. In total, 75.3%, 55.4%, and 45.7% of deaths in Brazil, Peru, and Chile, respectively, occurred in hospitals. After lockdowns were implemented, at-home deaths increased in all countries, and in-hospital deaths correspondingly decreased only in Chile. Our results suggest that COVID-19 has significantly affected rates of cancer mortality and place of death in Latin America.

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)0.003
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 score0.020
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.079
GPT teacher head0.415
Teacher spread0.336 · 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

Citations7
Published2023
Admission routes2
Has abstractyes

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