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Record W7024023353

Preventable sources of medication related morbidity and mortality at transitions in care for hospitalized patients

2019· other· en· W7024023353 on OpenAlexaboutno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2019
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsMultimorbidityContext (archaeology)Health careEmergency departmentPolypharmacyQuality of life (healthcare)Chronic conditionPopulationHospital careHealthcare systemMEDLINE
DOInot available

Abstract

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With the aging of the population and advances in medical care and public health, a growing proportion of individuals have multiple chronic conditions. Indeed, in Canada, almost half of those aged 65 years and older are living with multiple co-existing chronic conditions. The impact of multimorbidity is multifaceted, leading to lower quality of life, declines in functional status, higher rates of disability and increased mortality. For healthcare systems, multimorbidity leads to greater healthcare utilization, especially of high-cost services such as hospital stays and emergency department visits. One of the most complex aspects of providing optimal care to patients with multimorbidity is their use of many different medications. Approximately 60% of multimorbid patients use five or more medications which in turn increases their risk of drug-drug and drug-disease interactions, and adverse drug events (ADEs). Despite the clinical challenges associated with treating complex patients with many medications, evidence-based guidance for safe and effective prescribing in the context of multimorbidity remains limited. Patients with multiple chronic conditions are likely to require repeated admission to hospital, and once discharged, over one-third will be re-admitted to hospital within 90-days. However, reducing re-admissions in these complex patients has remained challenging since the reason for returning to hospital can include a number of interlinked patient-, provider- and healthcare system-level factors. The impact of patient medications is of significant interest since a large proportion of re-admissions are related to ADEs. When patients are hospitalized, they are often discharged on substantially different medications than those prior to admission. One might expect that discontinuations, additions or modifications to patient drug regimens during hospitalization would reduce the likelihood of adverse health outcomes after discharge. However, the extent to which patients actually adhere to hospital medication changes or the appropriateness of these changes is not known. The impact of these factors on short-term patient health outcomes after discharge is also unknown.The overarching goal of this thesis was to explore potentially preventable sources of medication-related morbidity and mortality in hospitalized patients in the transition between hospital and home. Three studies were completed to accomplish this goal.Objective Study 1: Estimate the incidence and determinants of non-adherence to hospital medication changes in the 30-days after hospitalizationObjective Study 2: Estimate the association between non-adherence to medication changes made at hospital discharge on the risk of re-admissions, emergency department visits and death in the 30-days post dischargeObjective Study 3: Estimate the incidence of potentially inappropriate medications prescribed at hospital discharge and their impact on re-admissions, emergency department visits and death as well as drug-related adverse events in the 30-days after discharge

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)0.008
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 score0.024
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.015
GPT teacher head0.252
Teacher spread0.237 · 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
Published2019
Admission routes1
Has abstractyes

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