Single Institute Experience With Methylphenidate and American Ginseng in Cancer-Related Fatigue
Bibliographic record
Abstract
BACKGROUND: Single therapy with methylphenidate or American ginseng contributes to the reduction in cancer-related fatigue (CRF) with different pharmacologic mechanisms and is relatively safe. However, the safety and efficacy of treating CRF with methylphenidate and AG combination therapy is unknown. AIM: The primary objective was to assess the clinical safety and the change in fatigue with numerical rating scale (NRS) on the Edmonton Symptom Assessment Scale (ESAS) after intervention with methylphenidate and AG combination therapy. METHODS: We reviewed the electronic medical records of 857 patients seen in our Palliative Medicine outpatient clinic between February 1, 2015, and December 31, 2015. Fatigue was assessed by NRS on ESAS. Toxicity was reviewed on clinician's documents. RESULTS: We identified 28 patients who were prescribed a combination of methylphenidate (10-40 mg/d) and AG (2000 mg/d). Ten patients did not comply with the combination therapy. Three patients had stage 2 adverse effects. Fifteen patients completed prescribed combination therapy per instructions. The mean time interval between pre- and postintervention follow-up was 30.5 days (standard deviation [SD]: 7.78). There was a significant reduction in the fatigue score (mean score 6.93-4.13) from the pre- to postscore records (mean: -2.8; SD: 1.61; P < .0002* [*refers to statistically significant]). Sixty percent of patients reported significant reduction in fatigue (cutoff value: ≥3; reduction in fatigue score from baseline: 80% ≥2, 60% ≥3, and 46.7% ≥4). CONCLUSION: In our retrospective medical record review, the combination treatment of methylphenidate and AG had no discernible associated toxicities and showed potential clinical benefit in CRF.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".