Bibliographic record
Abstract
Borderline personality disorder and DBT Welcome to today's scenario. We will again be using a virtual patient to explore some issues in mental health communications. The cases will work best for you if treat this virtual patient as you would a real patient. This case study includes content related to mental illness and addcitions. Sometimes these discussions can trigger unresolved personal experiences. If you need support please reach out to your family, physician, local ditress center, or if this is an emergency please go to your local emergecy department. James is a 30 year old male. Throughout his twenties he struggled to keep a job for longer than a couple months. James has also had one prior admission to an acute mental health unit for a failed suicide attempt. He had tried to hang himself from the garage ceiling using a belt, but his partner at the time found him after getting home from work and cut him down. It was noted that during this prior admission, James shared several different stories about why he tried to kill himself with the healthcare team. He had also threatened to report the nursing staff to the unit manager if they did not meet his needs and frequently engaged in self-harm behaviors on the unit. These behaviors included cutting his forearms with a razor and banging his head when he became emotionally dysregulated.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.129 | 0.094 |
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".