Methodological exploration on the construction of a traditional Chinese medicine nursing expert consensus based on evidence—taking stroke as an example
Bibliographic record
Abstract
To explore the methodology of the evidence-based expert consensus formulation process of traditional Chinese medicine (TCM) nursing taking stroke as an example. First, preliminary and comprehensive presentation of all stroke-related symptoms and corresponding TCM nursing techniques involved were revealed through bibliometric analysis. Then, selection of stroke symptoms and TCM nursing techniques for inclusion in the consensus was performed using an expert consultation method. Next, we determined the search strategy for a precise evidence search; conducted an evaluation of evidence quality and the grade of the evidence; and completed evidence extraction, evidence analysis, and evidence synthesis based on the included symptoms and TCM nursing techniques. The Delphi method was then applied to determine the strength of each recommendation and the choice of nursing care points by referring to the Grading of Recommendations, Assessment, Development, and Evaluations grid. Finally, we conducted an external expert validation of the Delphi results to form an expert consensus guideline. Through the bibliometric analysis, 22 stroke symptoms and 18 TCM nursing techniques were identified in the literature. Then, after expert consultation, 22 symptoms and 111 pairs of symptoms combined with TCM nursing techniques were selected for the evidence search. Evidence integration yielded 10 stroke symptoms corresponding to 29 bodies of evidence; these 10 symptoms were retained through the Delphi consultation, and recommendation strength results for 26 recommendations were obtained. A total of 9 symptoms were further retained for expert external validation to form 24 recommendations, with a recommendation process score range of 7.64–9.99 points and a more scientific and standardized recommendation-formation process. Owing to the current limited conditions of evidence-based resources for TCM nursing, the present consensus-building process represents only a preliminary exploration of an evidence-based expert consensus for TCM nursing to provide a reference for a more scientific and standardized methodology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.347 | 0.484 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.028 | 0.020 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".