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Record W2560993152 · doi:10.5770/cgj.19.257

Perspectives from Geriatric In-patients with Heart Failure, and their Caregivers, on Gaps in Care Quality

2016· article· en· W2560993152 on OpenAlexaffvenueabout
Nahid Azad, Geneviève Lemay, J. Li, Michael Benzaquen, L.R. Khoury

Bibliographic record

VenueCanadian Geriatrics Journal · 2016
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsMedicineGeriatricsLife expectancyGeriatric careNursingMedical emergencyFamily medicinePsychiatryPopulation

Abstract

fetched live from OpenAlex

BACKGROUND: Evidence indicates that care experiences for complex HF patients could be improved by simple organizational and process changes, rather than complex clinical mechanisms. This survey identifies care gaps and recommends simple changes. METHODS: The study utilized both quantitative and qualitative methods at The Ottawa Hospital, Geriatric Medical Unit during a three-month period. RESULTS: Nineteen patients (average age 85, 12 female) surveyed. Twelve participants lived alone. Fourteen lived in own home. Four patients had formal home-care services. Fifteen relied on family. Gaps were identified in in-patient practice, discharge plan, and discharge summary implementation feedback. Only five participants had seen cardiologist or specialist. Half of patients did not know if they were on a special HF diet. Participants did not recall receiving information on life expectancy but were comfortable discussing EoL care and dying. HF-specific management recommendations were mentioned in only 37% of discharge summaries to PCPs. CONCLUSION: The results provide the starting point for a quality assurance and process re-engineering program in GMU. Organization change is needed to develop and integrate a cardiogeriatric clinical framework to allow the cardiologist, geriatrician, and PCP to actively work as a team with the patient/caregiver to develop the optimal care plan pre- and post-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 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 score0.092
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.008
GPT teacher head0.227
Teacher spread0.219 · 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

Citations4
Published2016
Admission routes3
Has abstractyes

Explore more

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