MétaCan
Menu
← Back to cohort
Record W2767187621 · doi:10.1200/jco.2017.74.0340

Predictors of Posthospital Transitions of Care in Patients With Advanced Cancer

2017· article· en· W2767187621 on OpenAlexaboutno aff
Daniel E. Lage, Ryan David Nipp, Sara D’Arpino, Samantha M.C. Moran, P. Connor Johnson, Risa Liang Wong, William F. Pirl, Ephraim P. Hochberg, Lara Traeger, Vicki A. Jackson, Barbara J. Cashavelly, Holly S Martinson, Joseph A. Greer, David P. Ryan, Jennifer S. Temel, Areej El‐Jawahri

Bibliographic record

VenueJournal of Clinical Oncology · 2017
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsnot available
FundersNational Cancer InstitutePfizer
KeywordsMedicineDepression (economics)ConstipationAnxietyOdds ratioDistressCancerLogistic regressionQuality of life (healthcare)Internal medicineConfidence intervalPopulationPhysical therapyPsychiatry

Abstract

fetched live from OpenAlex

Purpose Patients with advanced cancer experience potentially burdensome transitions of care after hospitalizations. We examined predictors of discharge location and assessed the relationship between discharge location and survival in this population. Methods We conducted a prospective study of 932 patients with advanced cancer who experienced an unplanned hospitalization between September 2014 and March 2016. Upon admission, we assessed patients' physical symptoms (Edmonton Symptom Assessment System) and psychological distress (Patient Health Questionnaire-4). The primary outcome was discharge location (home without hospice, postacute care [PAC], or hospice [any setting]). The secondary outcome was survival. Results Of 932 patients, 726 (77.9%) were discharged home without hospice, 118 (12.7%) were discharged to PAC, and 88 (9.4%) to hospice. Those discharged to PAC and hospice reported high rates of severe symptoms, including dyspnea, constipation, low appetite, fatigue, depression, and anxiety. Using logistic regression, patients discharged to PAC or hospice versus home without hospice were more likely to be older (odds ratio [OR], 1.03; 95% CI, 1.02 to 1.05; P < .001), live alone (OR, 1.95; 95% CI, 1.25 to 3.02; P < .003), have impaired mobility (OR, 5.08; 95% CI, 3.46 to 7.45; P < .001), longer hospital stays (OR, 1.15; 95% CI, 1.11 to 1.20; P < .001), higher Edmonton Symptom Assessment System physical symptoms (OR, 1.02; 95% CI, 1.003 to 1.032; P < .017), and higher Patient Health Questionnaire-4 depression symptoms (OR, 1.13; 95% CI, 1.01 to 1.25; P < .027). Patients discharged to hospice rather than PAC were more likely to receive palliative care consultation (OR, 4.44; 95% CI, 2.12 to 9.29; P < .001) and have shorter hospital stays (OR, 0.84; 95% CI, 0.77 to 0.91; P < .001). Patients discharged to PAC versus home had lower survival (hazard ratio, 1.53; 95% CI, 1.22 to 1.93; P < .001). Conclusion Patients with advanced cancer who were discharged to PAC facilities and hospice had substantial physical and psychological symptom burden, impaired physical function, and inferior survival compared with those discharged to home. These patients may benefit from interventions to enhance their quality of life and care.

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 score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.144
GPT teacher head0.540
Teacher spread0.396 · 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

Citations40
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Clinical Oncology→Same topicPalliative Care and End-of-Life Issues→French-language works237,207→