Differentiation Between Primary Lateral Sclerosis and Amyotrophic Lateral Sclerosis
Bibliographic record
Abstract
BACKGROUND: Motor neuron diseases can affect the upper motor neuron and/or the lower motor neuron. Both amyotrophic lateral sclerosis (ALS) and primary lateral sclerosis (PLS) are motor neuron diseases, and there is much debate as to whether these are 2 separate disorders or simply 2 points on a continuum. OBJECTIVE: To determine which clinical features at onset and during follow-up could help differentiate between PLS and ALS. DESIGN: Retrospective study comparing patients with a diagnosis of PLS or ALS for differences in symptoms or signs at disease onset and during follow-up. SETTING: Tertiary referral center. Patients Six hundred sixty-one patients with ALS and 43 patients with PLS were included in the study. RESULTS: At presentation, stiffness was the only symptom that was significantly different between patients with PLS and patients with ALS (observed in 47% and 4% of patients, respectively; P<.001). During follow-up, limb wasting was rare in patients with PLS (2%, compared with 100% in patients with ALS; P<.001). Disease duration was significantly longer in patients with PLS compared with patients with ALS (mean +/- SD, 11.2 +/- 6.1 vs 3.8 +/- 4.2 years, respectively; P<.001). During the 16 years of follow-up, the mortality rate was significantly lower in patients with PLS compared with patients with ALS (only 33% vs 89%, respectively; P<.001). CONCLUSION: Our findings suggest that a patient presenting with spasticity who does not develop wasting within 3 years most likely has PLS.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".