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Record W2774969318 · doi:10.21037/apm.2017.10.06

Implementation issues relevant to outpatient neurology palliative care

2018· article· en· W2774969318 on OpenAlexaff
Benzi M. Kluger, Michael Persenaire, Samantha K. Holden, Laura T. Palmer, Hannah M. Redwine, Julie Berk, C. Alan Anderson, Christopher M. Filley, Jean S. Kutner, Janis M. Miyasaki, Julie Carter

Bibliographic record

VenueAnnals of Palliative Medicine · 2018
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsUniversity of Alberta
FundersNational Institute of Nursing ResearchPatient-Centered Outcomes Research Institute
KeywordsMedicineReferralPsychosocialPalliative careOutpatient clinicQuality managementFamily medicineAdvance care planningPatient satisfactionAmbulatory careNursingMedical emergencyHealth carePsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: There is growing interest in the application of palliative care principles to improve care for patients and families affected by neurologic diseases. We developed an interdisciplinary outpatient clinic for patients and families affected by neurologic disorders to better address the problems faced by our highest need patients. We have developed and improved this program over the past three years and share several of our most important lessons as well as ongoing challenges and areas where we see our clinic evolving in the future. METHODS: We provide a description of our clinic logistics, including key steps in the initiation of the clinic, and provide descriptions from similar clinics at other institutions to demonstrate some of the variability in this growing field. We also provide results from a formal one-year quality improvement project and a one-year retrospective study of patients attending this clinic. RESULTS: Our clinic has grown steadily since its inception and maintains high satisfaction ratings from patients, caregivers, and referring providers. To maintain standardized and efficient care we have developed materials for patients and referring physicians as well as checklists and other processes used by our interdisciplinary team. Feedback from our quality improvement project helped define optimal visit duration and refine communication among team members and with patients and families. Results from our chart review suggest our clinic influences advance care planning and place of death. Common referral reasons include psychosocial support, complex symptom management, and advance care planning. Current challenges for our clinic include developing a strategy for continued growth, creating a sustainable financial model for interdisciplinary care, integrating our services with disease-specific sections, improving primary palliative care knowledge and skills within our referral base, and building effective alliances with community neurologists, geriatrics, primary care, nursing homes, and hospices. CONCLUSIONS: Specialized outpatient palliative care for neurologic disorders fills several important gaps in care for this patient population, provides important educational opportunities for trainees, and creates opportunities for patient and caregiver-centered research. Educational initiatives are needed to train general neurologists in primary palliative care, to train neurologists in specialist palliative care, and to train palliative medicine specialists in neurology. Research is needed to build an evidence base to identify patient and caregiver needs, support specific interventions, and to build more efficient models of care in both academic and community settings.

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)0.134
metaresearch head score (Gemma)0.259
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.134
Threshold uncertainty score0.710

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1340.259
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0100.005
Scholarly communication0.0140.007
Open science0.0090.012
Research integrity0.0110.013
Insufficient payload (model declined to judge)0.0170.001

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 head0.260
GPT teacher head0.528
Teacher spread0.268 · 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 designNot applicable
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

Citations46
Published2018
Admission routes1
Has abstractyes

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