Unwanted patients and unwanted diagnostic errors
Bibliographic record
Abstract
Real people have real emotions that motivate their thinking. For example, the hopes of having a child can lead women with infertility to undergo courses of intense hormonal treatments and the fear of dying can lead men with prostate cancer to undergo surgical castration.1 Much of the attention towards advanced directives and discussions about goals of care are intended to document and legitimize a patient's emotions related to death and dying. Indeed, guidelines for physician-aided-dying suggest that a patient's emotions are sometimes more important than life itself.2 In contrast, the emotions of a physician are usually considered as unwanted intrusions into medical decision-making that have no legitimate relevance. Psychiatrists use the term ‘countertransference’ to denote a psychotherapist's emotions towards a patient. The basic concept is that a physician's own feelings may become entangled in the doctor–patient relationship and lead to missed diagnoses and ineffective care. Sigmund Freud first popularised the concept about a century ago emphasising how a physician's unconscious thoughts might include latent hostility or erotic feelings towards a patient.3 Different authorities over subsequent decades have also confirmed that countertransference is an undesirable but unavoidable component of medical diagnosis and treatment. The importance of these potentially disruptive physician emotions, however, is hard to judge in the absence of objective data. Schmidt et al present two articles testing whether disruptive patient behaviours might provoke unhelpful physician emotions and thereby decrease a physician's diagnostic accuracy.4 ,5 The studies involve clinical scenarios eliciting diagnostic judgements. Each scenario appeared in either a ‘negative’ or a ‘neutral’ version depending on changing a few fragments of text. The negative version described the patient with unpleasant features such as “He is angry about the long waiting time and starts speaking harshly …”. The neutral version described the same patient with innocuous …
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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 | 0.022 | 0.192 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".