Bibliographic record
Abstract
H ow does one make the diagnosis of psychotic depression ? How does one differentiate it from other conditions that may have similar symptoms? Although psychotic depression is usually referred to as a single homogeneous entity, it has several different presentations. Each deserves a different treatment and thus constitutes a different type of psychotic depression. There are other conditions that only resemble psychotic depression and whose treatments differ still further again. Distinguishing among these types and conditions is the essence of proper diagnosis, and it is crucial to helping the patient and avoiding harm. Problems with the DSM definition of psychotic depression The first issue is how the diagnostic manual of the APA called DSM deals with psychotic depression. The DSM discusses this issue under the category “major depression.” If a patient has a DSM major depression, the formalities of psychotic depression are brief, and simply the presence of hallucinations or delusions will qualify the patient for this diagnosis. The DSM stipulates nothing about the contents, form, severity, intrusiveness, or behavioral effects of any hallucinations or delusions. So, according to the DSM , psychotic depression is a major depression accompanied by hallucinations or delusions. Its formal and only name is “Major Depressive Episode, Severe With Psychotic Features.” It is classified as an episode that is symptomatic of an illness; the illness may be bipolar or unipolar, a single episode or recurrent.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".