Current practices in managing end-of-life existential suffering
Bibliographic record
Abstract
PURPOSE OF REVIEW: Within the context of palliative care, existential suffering (ES) can be an exclusive source of suffering or intertwined with physical pain and/or psychological and spiritual suffering. With newly emerging modalities for addressing this phenomenon and its increasing salience given that many patients cite ES as a significant contributing factor to requests for hastened death, a review of recent interventions for addressing ES at the end of life is timely. RECENT FINDINGS: This review of newer approaches to dealing with ES in the palliative context suggests some promising new modalities and pharmacological interventions, such as brain stimulation and the use of psychedelics. The use of other pharmacological interventions, such as palliative sedation and lethal injections, solely for the alleviation of existential distress remains ethically controversial and difficult to disentangle from other forms of suffering, not least because a clear clinical definition of ES has yet to emerge in the literature. SUMMARY: The evaluation of end-of-life (EOL) ES mitigating tools should also consider how broader contexts, such as institutional arrangements and barriers, and cultural factors may influence the optimal management of dying persons' ES in the palliative care setting.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".