Prognostic predictors relevant to end-of-life palliative care in Parkinson’s disease and related disorders: a systematic review
Bibliographic record
Abstract
Parkinson's disease and related disorders (PDRD) are the second most common neurodegenerative disease and a leading cause of death. However, patients with PDRD receive less end-of-life palliative care (hospice) than other illnesses, including other neurologic illnesses. Identification of predictors of PDRD mortality may aid in increasing appropriate and timely referrals. To systematically review the literature for causes of death and predictors of mortality in PDRD to provide guidance regarding hospice/end-of-life palliative care referrals. We searched MEDLINE, PubMed, EMBASE and CINAHL databases (1970-2020) of original quantitative research using patient-level, provider-level or caregiver-level data from medical records, administrative data or survey responses associated with mortality, prognosis or cause of death in PDRD. Findings were reviewed by an International Working Group on PD and Palliative Care supported by the Parkinson's Foundation. Of 1183 research articles, 42 studies met our inclusion criteria. We found four main domains of factors associated with mortality in PDRD: (1) demographic and clinical markers (age, sex, body mass index and comorbid illnesses), (2) motor dysfunction and global disability, (3) falls and infections and (4) non-motor symptoms. We provide suggestions for consideration of timing of hospice/end-of-life palliative care referrals. Several clinical features of advancing disease may be useful in triggering end-of-life palliative/hospice referral. Prognostic studies focused on identifying when people with PDRD are nearing their final months of life are limited. There is further need for research in this area as well as policies that support need-based palliative care for the duration of PDRD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".