Clinical Outcomes of Start-Low, Go-Slow Methadone Initiation for Cancer-Related Pain: What's the Hurry?
Bibliographic record
Abstract
BACKGROUND: Methadone has been shown to be effective for cancer pain. Most published switching methods are complete in less than three days, requiring very close supervision, usually in an inpatient setting. This need for hospitalization is a barrier to access. We present a large retrospective study of slow outpatient methadone starts and describe our starting method. METHODS: Charts were reviewed of patients referred to the Pain and Symptom Management/Palliative Care clinics at the six BC Cancer Agency's regional centers that underwent initiation of methadone for analgesia over a 14-year period. Patient characteristics, method of start, and outcomes of methadone treatment were recorded. RESULTS: Of the 652 identified patients, we were able to determine outcomes of methadone initiation in 564 (86.5%). Among these, 422 (74.8%) were deemed successful initiations, as determined by whether or not the patient remained on methadone at follow-up with subjective improvement in pain control, on a stable dose of methadone. Of the unsuccessful trials, 97/142 were primarily due to adverse events, 16 of which were considered serious enough to require hospitalization, including two due to sudden cessation of opioid therapy leading to withdrawal. Some of the included adverse events were not necessarily causal from the initiation of methadone, for example, development of bowel obstruction or delirium. Only one death occurred from a deliberate overdose of multiple medications, including methadone. CONCLUSIONS: Initiation of methadone for analgesia in ambulatory cancer patients can be done safely in an outpatient setting using a start-low go-slow method, and can be expected to be helpful in ∼75% of patients. Discontinuation is more likely to be for side effects than for inadequate analgesia. Access to methadone therapy can safely be widened by slow initiation, avoiding more dangerous rapid switching protocols and reducing the need for hospitalization.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".