A Systematic Review of Zero-markup Policy for Essential Drugs Effect on Medical Treatment
Bibliographic record
Abstract
Abstract Objective. This systematic review is conducted to synthesize recent empirical evidence of Zero-markup Policy for Essential Drugs Effect on Medical Treatment in China. Methods. We searched the PubMed, Embase, Scopus, and Cumulative Index to Nursing and Allied Health Literature (CINAHL) for all related studies published from inception to 30 April 2019 without restriction on language. In addition, grey literatures were captured through other sources, such as OpenGrey and Open Access Theses and Dissertations (OATD), to avoid selection bias. Methodological quality were evaluated using the PRISMA statement the Newcastle Ottawa Scale Collaboration tool. Results. Thirty-four full texts were initially searched, but only nine studies met our inclusion criteria. Most of studies indicated the significant reduction for both the total expense and drug expense per visit. Additionally, outpatient and inpatient services indicated increasing trends in annual patient-visits. Conclusions. In conclusion, the available limited, relative low-quality evidence does not support the long-term association between zero-markup policy for essential drugs and reduced medical expenditure. Further longitudinal studies that provide data for hospitals over a wider range of regions would make the economic effects more discursive.
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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 | 0.014 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".