Variation In Use of $4 Generic Program And Potential Savings Among Medicare Beneficiaries---Biostatistics Student's Internship Exit Report
Bibliographic record
Abstract
As an option to fulfill the MS thesis requirement at the Department of Biostatistics, I worked as an intern under the supervision of Dr. Yuting Zhang at the Department of Health Policy & Management, Graduate School of Public Health, University of Pittsburgh, from January to June 2010. During the internship, I have been fully involved in some of Dr. Zhang's projects and have made the following contributions. First, I consolidated different pharmacy event data and medical claims data obtained from multiple sources into several analytic databases for those projects. The end products in this step included the analytic datasets, data dictionary for each corresponding dataset, and the SAS programming codes. After completion of the dataset construction, I had opportunities to fully apply the statistical skills I have learned during my coursework on a specific project, entitled "Variation in the use of $4 generic prescription and potential savings among Medicare beneficiaries." Under the supervision of Dr. Zhang as well as collaborating with other colleagues, I played the leading role in data analysis, the interpretation of results and writing of a manuscript for publication.Public Health Relevance: Our research on these projects focused on evaluating the strengths and weaknesses of the Medicare prescription drug program, especially its effects on vulnerable American populations such as under-served minorities, patients with severe mental health and multiple medical conditions. Through our research, public policy might be improved to eliminate health disparities in populations. Our findings from the project have important policy implications for optimizing cost-effective use of prescription plans to the public.Through this half-year long internship, I have had great opportunities to learn study design, data management, statistical analysis and hypothesis testing in a real world setting, to apply statistics/econometrics knowledge to large existing data, to evaluate the effects of health care policy and interventions on medical spending and health outcomes. In addition, I have practiced advanced SAS programming skills in manipulating the large datasets.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".