The Criteria for Diagnosing Amyotrophic Lateral Sclerosis May Be Unsuitable for Clinical Use
Bibliographic record
Abstract
Background: The existing diagnostic criteria for definite, probable and possible amyotrophic lateral sclerosis (ALS) require at least one upper motor neuron (UMN) sign. However, we think some cases of ALS or motor neuron disease may be not able to fulfill the required criteria. We investigated whether the clinical presentation and course of patients with ALS or motor neuron disease fulfill the required criteria or not. Methods: In total, 296 patients who were diagnosed with ALS or motor neuron disease in the institute in the past 15 years were retrospectively investigated and analyzed. Results: In total, 108 patients (36.5%) who exhibited lower motor neuron (LMN) signs but not UMN signs at the early stage of the disease were diagnosed with ALS or motor neuron disease. Sixty-four of these 108 patients (59.2%) who developed respiratory failure and swallowing difficulty or UMN signs during the 5-year follow-up period were diagnosed with clinical ALS. No significant difference in the survival probability was observed between patients who exhibited both UMN and LMN signs and patients who exhibited only LMN signs. Conclusion: These findings suggest that some of motor neuron disease cases who only exhibit LMN signs can be diagnosed as ALS. I suggest the revised El Escorial Criteria for the Diagnosis of ALS may be unsuitable for clinical use. J Neurol Res. 2016;6(4):57-64 doi: http://dx.doi.org/10.14740/jnr383w
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".