Role of gabapentin in reducing the need for high-risk medications in patients with stable severe neurological impairment
Bibliographic record
Abstract
Objective: The study was aimed at assessing the prevalence of pain behaviors in children with severe neurological impairment (SNI), as well as the use of prescribed pain behavior medications, and the effects of gabapentin initiation on behaviors and use. Methods: A pre-post study was conducted on data from 11 patients with SNI who received gabapentin at a children's hospital in Canada. Symptoms and the use of high-risk pain behavior medications were assessed before and after gabapentin initiation and titration. Results: Pain was identified as a primary concern in most patients (8/11 [73%]) before gabapentin initiation. Dystonia was the most prevalent pain behavior (6/11 [55%]). Of the 11 patients, eight (73%) were taking benzodiazepines for symptom management, four (36%) were taking opioids, and one was taking a hypnotic sedative. Symptom improvement was observed in 10/11 (91%) patients after gabapentin initiation and titration. The use of benzodiazepine decreased in 6/8 (75%) patients, opioid use decreased in 3/4 patients, and hypnotic sedative use decreased in 1/1 patient. Successful discontinuation occurred for benzodiazepines in 5/8 (62.5%) patients, opioids in 1/4 (25%) patients, and hypnotic sedatives in 1/1 patient. Conclusions: Prescription medications with substantive risks, including benzodiazepines, opioids, and hypnotic sedatives, were used with high prevalence for pain behaviors in children with SNI. This study revealed an association between gabapentin initiation, and improved symptom burden and decreased use of the three medications.
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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 | 0.000 | 0.002 |
| 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) | 0.001 | 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".