Regional and Temporal Variation in Receipt of Gabapentinoid and SSRI/SNRI Therapy Among Older Cancer Survivors in the United States
Bibliographic record
Abstract
Opioids and benzodiazepines (BZD) are commonly prescribed for older cancer survivors with co-occurring pain and anxiety. The prescribing rate of gabapentinoids (GABA), Selective Serotonin Reuptake Inhibitors (SSRIs), and Serotonin-Norepinephrine Reuptake Inhibitors (SNRIs) in the general population has increased as opioid/BZD alternatives, but little is known on temporal/regional trends in use of these alternatives among older cancer survivors. A retrospective cohort study using SEER-Medicare data was conducted. Patients aged ≥ 66 years, diagnosed with breast, colorectal, prostate, or lung cancer as their first cancer diagnosis any time from 2000 to 2015 and who were alive more than 5 years after cancer diagnosis, were eligible for inclusion. Temporal trends varied by region (p < 0.0001) and opioid-naïve status (p < 0.0001). Compared to 2013, GABA and SNRI use increased, while BZD and opioid use decreased. All regions experienced declines in opioid use. From 2013 to 2018, all regions saw an increase in GABA use, with a decline in 2020. GABA prescriptions increased more in opioid-naïve groups compared to non-opioid-naïve patients. The yearly trends in GABA and SSRI/SNRI use varied by region among older cancer survivors. Clinical practice variation suggests needs for further research on improving consistency and quality of cancer care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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; 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".