MA Prescription Monitoring Program County-Level Data Measures (2018 Quarter 2)
Bibliographic record
Abstract
The Department of Public Health's (DPH) Prescription Drug Monitoring Program (PMP) serves as a repository of data for all prescription drugs dispensed statewide, including those prescriptions that are sought after for illicit and non-medical use and thus represent the highest potential for abuse (federal Schedules II -V, including certain narcotics, stimulants and sedatives).The PMP also enables prescribers and dispensers to access a patient's prescription history and can be used as a clinical decision-making tool, allowing the provider to have a holistic view of the patient's medications.When interpreting PMP county-level data, it is important to emphasize that increases or decreases in a single measure may not indicate an increase or decrease in prescription misuse or abuse.Put simply, use does not always equate to abuse.There are many factors that might explain an unusually high rate of prescribing in a given area.For instance, an area which contains a large number of residents in long-term care facilities may result a high rate of opioid prescribing.These datasets inform critical discussions about opioid prescribing, provide an important baseline to better inform future policy decisions and allow the state and stakeholders to more meaningfully measure whether policy initiatives are effective.Effective October 6, 2014, all hydrocodone combination drug (HCD) products (e.g., Vicodin) were reclassified from Schedule II I to Schedule II.This reclassification during the last quarter of 2014 makes comparisons over time difficult to interpret.Beginning with CY 2015 data, reports of Schedule II products will include all HCD prescriptions.Individuals with activity of concern "thresholds" for this report are based on a 3-month time period.MDPH also releases an annual county-level report that provides thresholds that are based on a 12-month time period.Although the numbers (or rates) generated may appear to be comparable, they represent different time periods and are NOT an apples-to-apples comparison.The results are only comparable when the thresholds (e.g., 4 different providers and 4 different pharmacies), time interval (e.g. over a three-month period), and drug products analyzed (e.g.Schedule II opioids) are the same.Meaning, the total number (or rates) of individuals who received Schedule II-V opioid prescriptions from 4 or more providers and had them filled at 4 or more pharmacies in a 3-month period cannot and should not be compared with the total number of individuals (or rates) who received Schedule II-V opioid prescriptions from 4 or more providers and had them filled at 4 or more pharmacies in a 12-month period. MA
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads 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".