P.078 Clinical Milestones in PSP and MSA may be Appropriate Triggers for Palliative Care Intervention
Bibliographic record
Abstract
Background: Progressive supranuclear palsy (PSP) and multiple system atrophy (MSA) are progressive neurodegenerative disorders with complex symptom burden and unpredictable disease trajectories. The ideal timing of palliative care interventions is uncertain given the variable natural history of both diseases. Methods: A systematic review was conducted to identify publications investigating predictors of survival in PSP and MSA. A medical librarian assisted to ensure comprehensive search strategy. Relevant literature on palliative care in PSP and MSA was also reviewed. Results from both searches were qualitatively combined in order to suggest triggers for targeted palliative care throughout the disease trajectory. Results: ‘Milestones’ are well documented and clinically relevant disease points that prompt further care. Important milestones include: frequent falls, cognitive impairment, unintelligible speech, severe dysphagia, wheelchair dependence, urinary catheterisation, and nursing home placement. PSP-Richardson syndrome accumulates milestones earlier than PSP-Parkinsonism or MSA. Many PSP patients already have falls and cognitive impairment at the time of diagnosis. Time from milestone to death is variable. Conclusions: Milestones can be used to trace disease progression and help predict survival. Clinical milestones are likely to be important triggers for targeted palliative care interventions including the early incorporation of a palliative approach to care or referral to specialised palliative care services.
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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 | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".