Pharmacotherapy administered during the intervention of emergency medical teams to people with mental disorders – a two-year observation
Bibliographic record
Abstract
The material consists of Dispatch Orders and Emer gency Medical Services Forms.Two periods were observed: period I (1 March 2019 -29 February 2020) and period II (1 March 2020 -28 February 2021).The database was prepared in Microsoft Excel using MS Office 2016 for Windows 10.The variables were described using descript ive statistics.The following measures were calculated for interval variables: mean (M) and standard deviation (SD).For categorical variables, the following measures were calculated: number (n) and frequency (%).Results.During the two-year period of analysis, 14,972 dispatch orders (I -7,531; II -7,441) were carried out by the Medical Rescue Teams in the examined operational area.The partial target of the analysis (patient with mental disorders) was met in 862 incidents (5.75% of the total).In 92 Medical Rescue Team interventions, pharmacological agents were administered to patients with mental disorders.This was a total of 100 drugs, most often hydroxyzine (41%), diazepam (33%), captopril (6%), and multi-electrolyte fluid MEF500 (6%).Patients were more often men (53.26%).Conclusions.Most Medical Rescue Team interventions requiring the use of drugs are associated with alcohol abuse and a strong stress reaction.The decision to administer drugs at the pre-hospital stage must be well AbstrAct Objectives.Assessment of the frequency of use and the type of drugs administered during the intervention of the Medical Rescue Teams to patients with mental disorders. Material and methods. The study includes a retrospective analysis of Medical Rescue Team interventions.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".