Applying futility in psychiatry: a concept whose time has come
Bibliographic record
Abstract
Since its introduction in the 1980s, futility as a concept has held contested meaning and applications throughout medicine. There has been little discussion within the psychiatric literature about the use of futility in the care of individuals experiencing severe and persistent mental illness (SPMI), despite some tacit acceptance that futility may apply in certain cases of psychiatric illness. In this paper, we explore the literature surrounding futility and argue that its connotation within medicine is to describe situations where patients (or their substitute decision-makers) believe that interventions will almost certainly provide no meaningful benefit. We then provide two arguments in support of the use of futility within the care of individuals experiencing SPMI: that some SPMI can be considered a terminal illness, and that the risk-benefit ratio is a dynamic entity such that futility can help describe what Gillett calls the 'risk of unacceptable badness' when it comes to considering how an intervention might impact a patient's quality of life. We posit that capacity should not pose an obstacle to declaring futility when caring for individuals experiencing SPMI and explain how futility is not antithetical to recovery in mental health. Finally, we describe how using futility within psychiatric practice can allow for a reorientation of care by signalling the need to shift to a palliative approach.
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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 | 0.037 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.136 |
| Scholarly communication | 0.013 | 0.022 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.012 | 0.021 |
| Insufficient payload (model declined to judge) | 0.003 | 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".