To Medicate Or Not to Medicate, When Diagnosis Is In Question: Decision-Making in First Episode Psychosis
Bibliographic record
Abstract
OBJECTIVE: This paper reports on a brief survey of clinicians' judgements when making treatment decisions in the context of diagnostic uncertainty. Specifically, attitudes and opinions were sought from practising consultant psychiatrists regarding two key areas of clinical decision-making in first episode psychosis (FEP), namely, when to initiate medication and, how long to continue treatment. METHOD: Interviews were conducted with consultant psychiatrists using a combination of structured and semi-structured questions that examined and explored pharmacological treatment decisions in FEP. RESULTS: Twenty-three consultant psychiatrists participated in the interviews. The threshold to initiate pharmacological treatment was lower when a risk to self or others is present, when symptoms are primarily positive, when the patient is in distress, or where there is a family history of mental illness. Atypical antipsychotics are routinely used as front-line medication in FEP and the choice of medication is determined largely by their likely side effect profile. However, the greater the perceived efficacy, the greater the anticipated tolerability burden. The ideal duration of treatment is considered to be 1-2 years in instances of full remission, and 5 years where only a partial response has been achieved or where recovery has not been sustained. CONCLUSIONS: The 'first episode' represents a unique period in the management of psychosis where by definition there is no history of pattern of illness, diagnostic certainty is rare, and the patient usually does not have any prior exposure to medications. Therefore, each management decision needs to be considered following a risk benefit analysis which takes into account the context of the individual.
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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 | 0.018 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".