Implementation Plan of the Clinical Practice Guideline for the Early Detection, Diagnosis and Treatment of Patients With Alcohol Abuse or Dependence in a Colombian Hospital
Bibliographic record
Abstract
OBJECTIVE: Implement a Clinical Practice Guideline for the early detection, diagnosis and treatment of the acute phase of intoxication of patients with alcohol abuse or dependence in the priority consultation services and outpatient consultation of the E.S.E Cari Neurosciences Hospital of the city of Barranquilla.INTRODUCTION: Alcoholism is the first drug addiction in many countries of the world. It affects all ages, both sexes and in almost all social groups. Alcohol is estimated to cause nearly 4% of deaths worldwide and is one of the top 20 causes of disability-adjusted life years (DALYs), either due to associated neurological and / or psychiatric conditions (38%), accidental injury (28%) or intentional injury (12%). In Colombia, alcohol consumption is widely accepted and promoted. The life prevalence of psychoactive substance use disorders is 10.6%, and alcohol abuse is the most prevalent disorder.MATERIALS & METHODS: An implementation plan for a management CPG for the early detection, diagnosis and treatment of an acute phase of intoxication of patients with alcohol abuse or dependence will be developed, according to the manual methodology of implementation of guidelines Clinical practice based on evidence from the Colombian Ministry of Health.CONCLUSIONS: The CPGs contain recommendations with the best clinical evidence available. Therefore, it is necessary that they be adopted by users, for which it is necessary to follow the dissemination, dissemination and implementation plan, address the intrinsic and extrinsic obstacles and facilitators of the CPGs and monitor the indicators described to measure the process of implementing CPGs in clinical practice.
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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 | 0.037 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".