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Record W6945590092 · doi:10.25384/sage.c.6429808.v1

Changes in mortality for the general population and individuals with pre-pandemic acute care for alcohol or opioids during the COVID-19 pandemic in Ontario, Canada

2023· other· en· W6945590092 on OpenAlexaffabout

Bibliographic record

VenueSage Journals Data · 2023
Typeother
Languageen
Field
Topic
Canadian institutionsInstitute for Clinical Evaluative SciencesOttawa HospitalBruyèreUniversity of Ottawa
Fundersnot available
KeywordsPandemicPopulationAlcohol use disorderAlcoholEmergency departmentExcess mortalitySubstance useAcute care

Abstract

fetched live from OpenAlex

Rates of alcohol- and opioid-related harms, including emergency department (ED) visits, hospitalizations, and deaths caused by alcohol or opioids, have increased since the beginning of the pandemic., It has been hypothesized that these increases in harms were driven by increased substance use by individuals with a prepandemic alcohol-use disorder (AUD) or opioid-use disorder (OUD) rather than by greater substance use during the pandemic in the general population., Despite the face validity of these concerns, there is limited data on changes in mortality for individuals with prepandemic substance use disorders (SUDs). This study compared excess mortality during the COVID-19 pandemic between the general population and individuals who received acute care (ED visit or hospitalization) for alcohol or opioids prepandemic.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.033
Threshold uncertainty score0.238

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.129
GPT teacher head0.383
Teacher spread0.254 · 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
Published2023
Admission routes2
Has abstractyes

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