HAMA焦虑排除判定规则
依据汉密尔顿焦虑量表(HAMA)总分阈值(<7分),在HAMA施测合规且效度可信前提下,系统性排除焦虑障碍共病,支撑单轴抑郁工作诊断决策。
npx skills add ECNU-ICALK/AutoSkill --skill HAMA焦虑排除判定规则 --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# HAMA焦虑排除判定规则 依据汉密尔顿焦虑量表(HAMA)总分阈值(<7分),在HAMA施测合规且效度可信前提下,系统性排除焦虑障碍共病,支撑单轴抑郁工作诊断决策。 ## Prompt 当HAMD确认重度抑郁(如总分≥24)且HAMA已规范施测、效度指标有效(如L/F量表无异常)时:提取HAMA总分;若总分<7分,则判定为‘无焦虑状态,可排除焦虑症’;该结论须以完整语句形式明确记录于个案概念化文档‘2.2评估与诊断’节,作为诊断依据链组成部分,并终止焦虑专项干预路径。 ## Objective 防止因焦虑症状漏判导致的抑郁单轴诊断误用 ## Applicable Signals - HAMA总分<7分 - 临床观察无显著焦虑行为表现(如坐立不安、过度担忧、躯体主诉集中于焦虑维度) ## Contraindications - HAMA未施测、施测不完整或存在严重作答效度问题(如随机作答、矛盾条目高分) - 存在未控制的甲状腺功能亢进、心律失常等可致焦虑样症状的躯体疾病 ## Workflow Steps - 1. 确认HAMA施测合规性及效度指标有效(含L/F量表核查) - 2. 提取HAMA总分 - 3. 比对阈值:总分<7 → ‘无焦虑状态’;总分≥7 → 进入焦虑共病评估流程 - 4. 在个案概念化中明确书写结论句:‘HAMA总分X分(<7),无焦虑状态,可排除焦虑症’ - 5. 将该结论归入诊断依据链,支撑单轴抑郁工作诊断 ## Constraints - 必须同步核查HAMA效度量表(如L、F量表)以排除装病或随意作答 - 不得将HAMA单项高分(如‘紧张’‘害怕’)单独作为焦虑存在依据,须依赖总分阈值 ## Cautions - HAMA对青少年焦虑敏感性有限,若来访者有回避表达、躯体化倾向或语言发展滞后,需结合行为观察与照料者访谈交叉验证 - 分数临界(如6分)不视为安全排除,应标注‘焦虑可能性低,暂不诊断,持续监测’ ## Output Contract - 书面结论语句:‘HAMA总分<7分,无焦虑状态,可排除焦虑症’,含具体分值、判定依据(HAMA总分阈值标准)及诊断意义说明(支撑单轴抑郁工作诊断),存档于个案概念化文档‘2.2评估与诊断’节 ## Files - `references/evidence.md` - `references/evidence_manifest.json` ## Triggers - HAMD总分≥24(提示重度抑郁) - 需鉴别焦虑是否共病 - HAMA已规范施测且结果可信
- Prompt
- Objective
- Applicable Signals
- Contraindications
- Workflow Steps
- Constraints
- Cautions
- Output Contract
- Files
- Triggers
What does the HAMA焦虑排除判定规则 skill do?
依据汉密尔顿焦虑量表(HAMA)总分阈值(<7分),在HAMA施测合规且效度可信前提下,系统性排除焦虑障碍共病,支撑单轴抑郁工作诊断决策。
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill HAMA焦虑排除判定规则 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ECNU-ICALK/AutoSkill, a repository with 539 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.
