Bibliographic record
Abstract
I n psychiatry , the past can be a storehouse of wisdom. Very often the older diagnoses and remedies are superior to the newer patent-protected drugs and industry-promoted diagnoses. It is for that reason that some understanding of the history and diagnosis of psychotic depression is useful. We know historically that psychotic depression seems to have a specific response to treatment and a specific prognosis. That matters today. If you as a physician did not have a distinctive treatment to offer, it would not matter so much whether psychotic depression differs from other illnesses, just an academic exercise, really. But here therapeutic choices exist. Also, there are different kinds of psychotic depression, with different responses to treatment (see Chapter 8). Finally, you can tell a troubled family what they can expect in the future. So these diagnostic hairsplittings are not academic. Psychiatry has long yearned for such choices. As Robert Gaupp, professor of psychiatry in Tübingen, said in 1926: What we want and need as physicians are diagnoses and classifications that don't let us down when we're in front of a living human being, a human being who stands ill before us and whose apprehensive family wants to know what's going to happen, recovery or chronic suffering, return to normality or death after years or decades of decline. What's going to happen? What should we do? ” (Gaupp, 1926; emphasis in the original) So chiseling psychotic depression from the mass of undifferentiated depressive illness is quite a practical exercise.
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 | 0.000 | 0.001 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".