Modafinil for attentional and psychomotor dysfunction in advanced cancer: a double-blind, randomised, cross-over trial
Bibliographic record
Abstract
Cognitive impairment seems to be highly prevalent in patients with advanced cancer. Modafinil, a novel vigilance and wake-promoting agent, may be an alternative treatment. We wanted to investigate this treatment on attentional and psychomotor dysfunction in cancer patients. 28 cancer patients with a tiredness score of 50 mm or more on a scale of 0 to 10 (0=no tiredness, 10=worst possible tiredness) and Karnofsky Performance Status 40-70 were included. All medications were kept stable during the trial despite short acting opioids for breakthrough pain. On day 1 the patients were randomly assigned to receive 200 mg Modafinil orally or placebo and on day 4 they crossed-over to the alternative treatment. Finger Tapping Test (FTT), Trail Making Test (TMT) and Edmonton Symptom Assessment System (ESAS) were evaluated before tablet intake and again 4, 5 hours after. FTT for the dominant hand as well as TMT were statistically significantly improved on modafinil (p-values=0.006 and 0.042, respectively). On ESAS, depression and drowsiness also improved statistically significantly (p-values=<0.001 and 0.038, respectively). Modafinil in a single dose regimen was significantly superior to placebo regarding two cognitive tests of psychomotor speed and attention. Furthermore subjective scores of depression and drowsiness were significantly improved by modafinil.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".