Ethical Psychotherapeutic Management of Patients with Medically Unexplained Symptoms: The Risk of Misdiagnosis and Harm
Bibliographic record
Abstract
Abstract Management of medically unexplained symptoms (MUS) is undergoing a period of change. We see this in the recent breakdown of consensus on mental health management of quintessential medically unexplained conditions (like myalgic encephalomyelitis/chronic fatigue syndrome), and in recent work in bioethics suggesting that the issue of biological versus mental health management of MUS is fundamentally an ethical matter. For these reasons, it is important to think carefully about ethical aspects of MUS management in psychotherapeutic settings. In the first part of this chapter, the authors show how ambiguity in the term “MUS” leads to routine conflation of diagnostic uncertainty with psychological diagnosis for unexplained symptoms in medical settings. The second part of the chapter explores evidence suggesting that substantial harm results from a failure to draw that distinction in medical settings, and clarifies the psychotherapist’s obligations to avoid those harms. The authors then explore the risk for psychological harms when psychotherapists conflate diagnostic uncertainty with psychological diagnosis. Finally, they consider challenges to informed consent in psychotherapy for MUS. The chapter concludes with principles for ethical psychotherapeutic management of MUS.
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 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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".