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Symptom burden to predict health care utilization in hospitalized patients with incurable cancer.

2016· article· en· W2589476242 on OpenAlexaboutno aff
Ryan David Nipp, Areej El‐Jawahri, Samantha M.C. Moran, Sara D’Arpino, Connor Johnson, Daniel E. Lage, Risa Liang Wong, Yian Xiao, Harry VanDusen, William F. Pirl, Lara Traeger, Inga T. Lennes, Barbara J. Cashavelly, Holly S Martinson, Vicki A. Jackson, Joseph A. Greer, David P. Ryan, Ephraim P. Hochberg, Jennifer S. Temel

Bibliographic record

VenueJournal of Clinical Oncology · 2016
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAnxietyMoodNauseaDepression (economics)ComorbidityPopulationMarital statusCancerPhysical therapyPatient Health QuestionnaireInternal medicinePsychiatryDepressive symptoms

Abstract

fetched live from OpenAlex

98 Background: Patients with incurable cancer are often hospitalized and have frequent readmissions after discharge. Considering the high physical and psychological symptom burden in this population, we sought to investigate symptoms as predictors of hospital length of stay (LOS) and time to first unplanned readmission. Methods: We consecutively enrolled incurable cancer patients with unplanned hospital admissions from 9/2014-4/2016. Within the first 5 days of admission, we assessed physical (Edmonton Symptom Assessment System [ESAS]; scored 0-10) and mood symptoms (Patient Health Questionnaire 4 [PHQ-4]; scored categorically). We created summated ESAS total and physical symptom variables. To identify predictors of LOS we used linear regression and for time to readmission we used Cox regression, with all models adjusted for age, sex, marital status, comorbidity, education, cancer type and time since incurable diagnosis. Results: We enrolled 1,000 of 1,227 (81%) eligible patients (mean age = 63.4; 50% female; 66% married). Gastrointestinal (33%) and lung (18%) cancers were the most common. Mean hospital LOS was 6.2 days and 30-day readmission rate was 25%. Over half of patients reported moderate/severe fatigue, drowsiness, lack of appetite, pain and poor well-being. Over one-fourth screened positive for PHQ depression and anxiety. All physical and mood symptoms individually predicted for longer LOS. Pain, nausea, poor well-being, ESAS total, ESAS physical and PHQ anxiety predicted for shorter time to readmission. Conclusions: Hospitalized patients with incurable cancer experience a high symptom burden, which correlates with their health care utilization. Both physical and psychological symptoms predict for longer hospital LOS and shorter time to readmission. These findings can inform interventions targeting patients’ symptoms during hospital admissions in an effort to improve health care delivery and utilization. [Table: see text]

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)0.005
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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.063
GPT teacher head0.448
Teacher spread0.386 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

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