Has pain management improved over the last decade in the Rapid Response Radiotherapy Program?
Bibliographic record
Abstract
The management of cancer pain remains a significant clinical issue [1,2]. A systematic review and subsequent update by Deandrea et al.[1] and Greco et al. [2] report that the rate of inadequate pain management has decreased from 46.6% to 31.8%. However, our assessment of patients referred to the Rapid Response Radiotherapy Program (RRRP) indicates that improvement in pain management may not be as reported [3–6]. Widely used in literature and in systematic reviews, the Pain Management Index (PMI) was a tool developed in 1994 to provide a standardized method of quantifying the adequacy of pain management [7]. The PMI assigns an analgesic score for no pain medication, nonopioids (e.g., nonsteroidal anti-inflammatory drugs), weak opioids, and strong opioids as 0, 1, 2, and 3, respectively. Similarly, the patient's pain is scored with no pain being 0, mild pain being 1, moderate pain being 2, and severe pain being 3. The pain score is subtracted from the analgesic score, giving the PMI score as a measure of the adequacy of pain management. A negative value is interpreted as inadequate management and a value of greater or equal to 0 is considered adequate management. Our first assessment of the adequacy of pain management in patients referred to the RRRP did not directly use the PMI tool. Instead, the study defined undertreatment as patients reporting moderate/severe pain and given weak opioids, nonopioids, or no pain medications [3]. The study reported a decline in undermedicated patients from 1999–2001, and a subsequent increase from 2003–2006 [3]. A retrospective analysis was later done for the same time period using the PMI tool. Because of the slight difference in the classification of inadequate pain management, the numbers cannot be compared directly. Nonetheless, an overall increase of undermedication was identified using a logistic regression analysis (P < 0.0001) [4]. The most recent assessment of the adequacy of pain management at our center indicates a rate of undertreatment of 31% for 1999–2008 and 33% for 2009–2015 [5,6]. When comparing these two values, there is no change in the rate of inadequate pain management. Given these observations, the RRRP has not seen a decrease in patients that are inadequately medicated for their pain in the past few years. Over the past 2 decades the PMI has become the predominant method of assessing the adequacy of pain management. However, it has its shortcomings including the inability to include analgesic dose, method, or schedule of drug administration, and the exclusion of other adjuvant medications such as steroids into its scoring system [5,6,8]. Another issue noted frequently in literature is that the PMI will assign patients with severe pain on strong opioids a score of zero [4,6,9]. This will include patients on strong opioids that have not been adequately titrated or have opioid-resistant pain. In these cases, the patients should be classified as undertreated. Because of this, it systematically reports lower rates of undertreatment [8]. As stated by Greco, the PMI tool may be better suited for use as a process indicator given the ease in data collection and interpretation [10]. The shortcomings of the PMI limit the conclusions that can be made on pain management when this tool is used [10]. We suggest that there is an urgent need to focus on developing an assessment tool that will be able to accurately act as an outcome indicator to monitor the progress in cancer pain management. If we cannot accurately assess the adequacy of pain management, development of effective strategies for improving cancer pain management remains an elusive unmeasurable goal. Acknowledgements None. Financial support and funding None. Conflicts of interest There are no conflicts of interest.
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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 | 0.011 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".