Recombinant human granulocyte-colony stimulating factor administration for treating amyotrophic lateral sclerosis: A pilot study
Bibliographic record
Abstract
Granulocyte-colony stimulating factor (G-CSF) is used to mobilize CD34+ haematopoietic stem cells from the bone marrow to the peripheral blood. We proposed to use cell subsets induced by G-CSF to slow down disease progression in patients with amyotrophic lateral sclerosis (ALS). Patients with definite or probable ALS were assigned in a double-blind manner to receive G-CSF or placebo every three months for a year. The primary outcome measure was the functional decline, measured by the revised ALS Functional Rating Scale, Revised (ALSFRS-R) score. Secondary outcome measures included vital capacity, manual muscle strength, compound muscle action potential amplitudes, neurophysiological index, and McGill single item quality of life score (QoL). Thirty-nine patients were enrolled. Seventeen patients who received G-CSF and 18 who received placebo were evaluated. G-CSF was effective in mobilizing CD34+ to blood. The outcome measures used showed no statistically significant benefit, although there was a trend of slowing disease progression following two G-CSF treatments, as shown by lower slopes of ALSFRS-R and QoL in the first six treatment months. The treatment had no major side-effects. G-CSF administration in ALS patients caused successful mobilization of autologous bone marrow cells, but was not effective in slowing down disease deterioration.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".