The Medication Quantification Scale 4.0: An Updated Index Based on Prescribers' Perceptions of the Risk Associated With Chronic Pain Medications
Bibliographic record
Abstract
To quantify risks associated with drug utilization in the real world for the treatment of chronic pain (CP), an index called the Medication Quantification Scale (MQS) was developed in 1992 in the United States and last updated in 2003. This study aimed to update, adapt to the contemporary Canadian context, and validate a revised version of the MQS (the MQS-4.0). Step 1: An expert committee adapted the MQS to the Canadian clinical practice context. Step 2: An update of risk weights given to medication subclasses was achieved using a prescriber survey (weights were derived from median 0-10 scores given to each subclass). Step 3: Construct validity of the MQS-4.0 was assessed after applying risk weights to the medication use profile of persons living with CP covered by public drug insurance plan. Thirty-six medication subclasses were included in the MQS-4.0. A total of 207 prescribers (physicians, pharmacists, and nurse practitioners) participated in the perception survey; 10.63% identified as pain specialists. When risk weights were applied to prescription claims (n = 9,122), the MQS-4.0 score was associated (P < .05) with the MQS-III score and variables associated with polypharmacy (eg, Charlson Comorbidity Index, number of prescribers or health care visits). This study provides an updated index intended for adult populations based on prescribers' perceptions of the risk associated with CP medications that can be useful for clinical practice and research among persons living with CP in Canada. It will, however, be relevant to verify whether similar risk weights are obtained in future pain specialist surveys. PERSPECTIVE: The MQS-4.0 is an update of the MQS used for quantifying the risk associated with the use of analgesics/coanalgesics. Adequate psychometrics properties were found.
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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 | 0.009 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".