Interventional Pain Management for CancerPain: An Analysis of Outcomes and Predictors ofClinical Response
Bibliographic record
Abstract
BACKGROUND: Interventional procedures are offered routinely to patients seen in McGill University's interdisciplinary cancer pain management program. However, publications on these procedures are scarce, making it difficult to predict which patients may benefit from them. OBJECTIVES: We hypothesized that interventional pain procedures offered to cancer patients could provide relief of pain as well as other symptoms. Furthermore, some variables may predict the efficacy of such procedures. STUDY DESIGN: We conducted a retrospective chart review of interventional pain management procedures. SETTING: The procedures reviewed were conducted at the Cancer Pain Program and performed at the interventional suites of the McGill University Health Centre. METHODS: The retrospective chart review included interventional pain management procedures performed between June 2015 and March 2017. Demographic data, details about the underlying cancer and about the procedure and peripTrocedural patients' reported outcomes were recorded for analysis. RESULTS: Eighty-two of 126 procedures were included for analysis. Most patients presented with metastatic disease (75%). Eighty percent of the patients reported pain relief, with the average pain severity decreasing by more than 2 points on a 0-to-10 Numeric Rating Scale for pain (from 6.5 of 10 to 4.2 of 10). Forty-three percent of patients were considered responders (>= 50% pain relief). Responders also reported a significant decrease in fatigue, depression, anxiety, drowsiness, and improved well-being. Among responders, average daily opioid use decreased significantly, by 60% on average. None of the analyzed variables correlated with the response; however, psychosocial variables like anxiety and depression showed a nonsignificant trend towards predicting procedure failure. LIMITATIONS: The core limitations of this study are its size and retrospective nature. CONCLUSIONS: In this cohort of cancer pain patients, interventional cancer pain procedures provided effective pain relief and other benefits, including pain relief, reduced burden of symptoms, and reduction of opioid intake, while demonstrating a favorable safety profile. Patients with poorer ratings of depression and fatigue derived less benefit from procedures, suggesting that offering such procedures as part of patients' treatment plan would be sensible, rather than leaving interventions for later stages.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".