The Reporting of Clinical Acupuncture Research: What Do Clinicians Need to Know?
Bibliographic record
Abstract
UNLABELLED: It is presently unknown what the real impact of clinical acupuncture research on practitioners is, or what kind of specific information clinicians need to find on a published paper in this field. OBJECTIVES: To develop a pilot survey instrument to evaluate clinicians' information needs when reading acupuncture research papers, and then to use it to assess the relative importance that specific clinical details may have for clinicians when reading papers on the areas of acupuncture treatment for migraine/headaches and nausea/vomiting. METHODS: The survey instrument consisted of a list of 50 clinical details grouped in four areas: practitioners, patients, diagnostic procedures, and acupuncture treatment. Questions about the relative importance of these details regarding acupuncture research in general, and on the areas of migraine/headaches and nausea/vomiting in particular, were answered by 34 medical acupuncture practitioners attending a conference. RESULTS: Most clinical details were deemed important, with the highest rating for details concerning the acupuncture treatment (M = 3.25 +/- 0.43 on a scale from 0 = not at all important to 4 = very important), and diagnostic procedures (M = 2.91 +/- 0.33). Similar results applied to the research on migraine/headaches and nausea/vomiting. CONCLUSION: For acupuncture clinical research to have a real impact in daily practice, researchers need to be sensitive to the needs of clinicians and provide enough information about clinical details on the published papers. A survey instrument like this seems to be an appropriate tool to gather information about clinicians' needs.
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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 | 0.217 | 0.658 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.013 | 0.022 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".