The Use of <i>Pro Re Nata</i> or <i>Statim</i> Medications for Behavioral Control: A Summary of Experience at a Tertiary Care Children's Mental Health Center
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The present study aims to identify patterns for use of medication given pro re nata (PRN or "on an as needed [preordered] basis") or statim (STAT [a new order] or "at once, immediately") and their efficacy in controlling aggressive behavior in the mental health (MH) services environment. PRN and STAT medication data were combined and referred to as PRN throughout this article, as the data were not collected in a manner required to differentiate between PRN and STAT medication administration. Analyzed data were extracted from the clinical records of a sample of children and youth admitted for the first time to a tertiary MH center. MH Program patients (characterized by at least one Axis I psychiatric diagnosis [Axis I group]) were compared to Dual Diagnosis Program patients (characterized by an Axis I diagnosis in addition to an Axis II diagnosis of mental retardation [Axis II group]). Age, gender, Program (Axis I or II group), and the length of stay for treatment produced significant differences in the use of PRNs between the two groups. Further, the study investigated the precipitating factors leading to use of PRNs, in conjunction with the level of supervision and the de-escalation techniques used to avoid the use of PRNs. Axis I patients were more likely to endanger others, whereas Axis II patients were more likely to endanger themselves. Both groups of patients demonstrated a need for an increased level of supervision prior to the crisis. Olanzapine, chlorpromazine, and lorazepam were effective in calming patients and preventing further aggressive outbursts.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it