MétaCan
Menu
← Back to cohort
Record W4404837199 · doi:10.1370/afm.22.s1.6093

Declines in intensive care unit admissions in early COVID-19 pandemic among persons with dementia in three Canadian provinces

2024· article· en· W4404837199 on OpenAlexaboutno aff
Deniz Cetin‐Sahin, Claire Godard‐Sebillotte, Susan E. Bronskill, Andrea Gruneir, Laura C. Maclagan, Victoria Massamba, Machelle Wilchesky, Erik Youngson, Isabelle Vedel

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsPandemicDementiaCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Intensive care unitUnit (ring theory)MedicineGerontologyPsychologyPsychiatryVirologyDiseaseOutbreakInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Context: Providing critical care for people living with dementia (PLWD) during the COVID-19 pandemic outbreak was raised as a global concern. A task force commissioned by the Alzheimer Society of Canada published 7 principles to consider when planning access to scarce resources to ensure that respect for the dignity of PLWD is preserved. Objective: To measure the impact of the first two waves of the pandemic on intensive care unit (ICU) use among PLWD. Study Design: Retrospective historically controlled cohort study using population-level administrative health data. Setting: Alberta, Ontario, Quebec. Population: PLWD aged 65+. Methods: We identified two closed cohorts of PLWD on March 3, 2019 (pre-pandemic) and March 1, 2020 (pandemic) and stratified them by community and nursing home settings Outcome measures: Rates of intensive care unit admissions. Analysis: We used a 2-step meta-analytical approach. Step 1: Compared rates of ICU admissions in three 2020 periods (1st wave; interim period; 2nd wave) to the corresponding 2019 periods. Step 2: Conducted random effect meta-analyses on the provincial incident rate ratios (IRR) and 95% CIs. Results: Community cohorts included 160,288 (pre-pandemic) and 166,392 (pandemic) individuals. Nursing home cohorts included 91,646 (pre-pandemic) and 90,727 (pandemic) individuals. The rates of ICU admissions in the community were 25% (IRR=0.75 [0.69–0.82]) lower in the interim period and 24% lower in the second wave (IRR=0.76 [0.74–0.78]). Pre-pandemic rates of ICU admissions from nursing homes were higher than expected, showing differences between provinces (from 0.01 to 0.06 per 100-person week). In nursing homes, rates of ICU admissions were lower throughout the pandemic year: 29% (IRR=0.71 [0.69–0.73]) less in the first wave, 32% (IRR=0.68 [0.57–0.82]) less in the interim period, and 24% (IRR=0.76, [0.63–0.93]) less in the second wave. Conclusions: There is a need for a single hospital/ICU triage protocol, particularly for very frail older adults such as PLWD. Pre-pandemic high use of ICU from nursing homes is unexpected given that PLWD have advanced stage of dementia. This speaks to the need for better practices for advance care planning and for a global consensus on the level of frailty making the use of ICU futile. In the community, lower ICU admissions during the pandemic periods are surprising given that PLWD had more severe COVID infections. Future studies should explore provincial practices and policies.

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)0.009
metaresearch head score (Gemma)0.015
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 score0.038
Threshold uncertainty score0.277

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.011
Bibliometrics0.0040.008
Science and technology studies0.0020.001
Scholarly communication0.0020.000
Open science0.0030.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.039
GPT teacher head0.318
Teacher spread0.279 · 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
Published2024
Admission routes1
Has abstractyes

Explore more

Same topicIntensive Care Unit Cognitive Disorders→French-language works237,207→