癫痫及惊厥性癫痫持续状态医疗质量控制指标 Medical Quality Control Indicators for Epilepsy and Convulsive Status Epilepticus
Bibliographic record
Abstract
癫痫是世界卫生组织重点防治的五大神经精神疾病之一。2017年6月至今，我国国家神经系统疾病医疗质量控制中心癫痫质控专病组在充分参考国内外循证证据及既往经验的基础上，经过3批试点，逐步启动全国癫痫医疗质量控制指标体系工作，构建了覆盖我国31个省级行政区，130家质量控制哨点医院的国家癫痫及惊厥性癫痫持续状态医疗质量控制体系，并依托哨点医院反馈的诊疗指标数据，对既往医疗质量控制指标体系进行修订，最终纳入20个经过验证的癫痫与惊厥性癫痫持续状态医疗质量控制指标。2020年，国家卫生健康委员会办公厅发布了该指标体系。单病种医疗质量控制指标依托全国质量控制网络体系落实医疗质量控制与管理，旨在改进医疗质量、促进病种诊疗同质化。目前该指标体系已被纳入三级医院评审标准，在持续加强指标信息培训、分析和反馈的同时，不断推进各级医疗机构癫痫和惊厥性癫痫持续状态的规范诊治。 Abstract: Epilepsy is one of the five major neuropsychiatric diseases that the World Health Organization (WHO) focuses on prevention and treatment. From June 2017 to the present, the Epilepsy Working Group of National Center for Healthcare Quality Management in Neurological Diseases has launched the national epilepsy medical quality indicator system after three batches of pilots, based on full reference to domestic and overseas evidence-based evidence and previous indicator experience. It has built a medical quality control system for epilepsy and convulsive status epilepticus covering 130 sentinel hospitals in 31 provincial administrative regions across China. Regarding on the diagnosis and treatment indicator data feedback from sentinel hospitals, the system was revised based on the published quality control indicators. Twenty validated indicators in epilepsy and convulsive status epilepticus were included. In 2020, the General Office of the National Health Commission issued the indicator system. Disease quality control indicators rely on the national quality control network system to implement medical quality control and management, aiming at improving medical quality and promoting the homogenization of diagnosis and treatment. Currently, this indicator system has been incorporated into the accreditation standards for tertiary hospitals. In addition to strengthening the training, analysis and feedback of information on the indicators, the standardized diagnosis and treatment of epilepsy and convulsive status epilepticus in medical institutions at all levels are constantly promoted.
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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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".