Establishment and practice of the Traditional Chinese Medicine nursing clinic under the mode of Green treatment of tumor
Bibliographic record
Abstract
Taking the mode of Green treatment of tumor as the starting point, under the guidance of the concept of syndrome differentiation and treatment of internal diseases, the tumor-green nursing clinic mainly applies the tumor-green nursing technology to carry out symptom nursing and physical conditioning for tumor patients, extends the tumor-green nursing to the chronic disease management, carries out the whole process nursing of tumor patients, and makes the transformation of diagnosis and treatment service to mode of "prevention of disease". In the construction of tumor-green Traditional Chinese Medicine(TCM) nursing clinic, a series of standards, scientific management modes and modified outpatient operation quality control system have been formulated. At the same time, tumor-green nursing technical specifications and standards have been formulated, and a Chinese version of pan-Canadian Oncology Symptom Triage and Remote Support (COSTaRS) Scale has been introduced for the overall, dynamic and comprehensive evaluation of tumor patients. The establishment of tumor-green TCM nursing clinic is conducive to improve TCM nursing service ability, meet the individual health needs of patients, and promote the construction and development of TCM nursing clinic standardization. (肿瘤中医绿色护理门诊以肿瘤绿色治疗模式为出发点, 在辨证论治、内病外治理念的指导下, 主要应用肿瘤绿色调护技术, 对肿瘤患者进行症状护理和体质调理, 把肿瘤绿色调护延伸至肿瘤门诊慢病管理中, 实现了肿瘤患者全程护理, 实现了诊疗服务向“治未病”预防模式转换。在肿瘤中医绿色护理门诊建设上制订了一系列规范、科学的工作与管理模式及完善的门诊运行质量控制体系, 同时也制定肿瘤绿色调护技术规范和标准, 且引入CosTaRs本土化症状评估量表, 用于对肿瘤患者进行整体、动态、全面的评估。通过肿瘤中医绿色护理门诊的建立有利于提升中医护理服务能力, 满足患者个体化健康需求, 推进中医护理门诊标准化的建设与发展。)
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How this classification was reachedexpand
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".