Acupuncture
Bibliographic record
Abstract
1 INTRODUCTION 2 -- 1.1 CONTEXT 2 -- 1.2 OBJECTIVES AND METHODS 2 -- 1.3 LIMITATIONS 3 -- 2 ACUPUNCTURE IN BELGIUM: USERS, PRACTITIONERS AND PRACTICES 4 -- 2.1 NON-CONVENTIONAL MEDICINES: AN INCREASINGLY FREQUENT USE 4 -- 2.2 ACUPUNCTURE IN BELGIUM: GENERAL BACKGROUND 4 -- 2.3 WHO ARE THE PATIENTS? 4 -- 2.4 WHO ARE THE PRACTITIONERS? 6 -- 2.5 THE PATHWAY OF THE PATIENT 7 -- 2.5.1 Initial access to the non-conventional medicine 7 -- 2.5.2 The consultation 7 -- 2.5.3 Financial aspects 9 -- 2.5.4 Patient satisfaction 9 -- 3 IS ACUPUNCTURE CLINICALLY EFFECTIVE? 11 -- 3.1 EVIDENCE IN THE SCIENTIFIC LITERATURE 11 -- 3.2 PATIENT POINT OF VIEW 13 -- 3.3 WHICH RISKS? 13 -- 4 TRAINING 14 -- 5 LEGAL FRAMEWORK 15 -- 5.1 BACKGROUND 15 -- 5.2 THE COLLA LAW 15 -- 5.3 CONSEQUENCES OF PARTIAL EXECUTION OF THE COLLA LAW 17 -- 6 CONCLUSION 18 -- 7 REFERENCES 21
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.295 | 0.114 |
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".