Diagnostic delay in amyotrophic lateral sclerosis
Bibliographic record
Abstract
BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a progressive, fatal neurodegenerative disease, and the time from symptom onset to diagnosis remains long. With the advent of disease-modifying treatments, the need to identify and diagnose ALS in a timely fashion has never been greater. METHODS: We reviewed the literature to define the severity of ALS diagnostic delay, the various factors that contribute to this delay (including patient and physician factors), and the role that site of symptom onset plays in a patient's diagnostic journey. RESULTS: Diagnostic delay is influenced by general practitioners' lack of recognition of ALS due to disease rarity and heterogenous presentations. As a result, patients are referred to non-neurologists, have unnecessary diagnostic testing, and may ultimately be misdiagnosed. Patient factors include their illness behavior-which impacts diagnostic delay-and their site of symptom onset. Limb-onset patients have the greatest diagnostic delay because they are frequently misdiagnosed with degenerative spine disease or peripheral neuropathy. CONCLUSION: Prompt ALS diagnosis results in more effective clinical management, with earlier access to disease-modifying therapies, multidisciplinary care, and, if desired, clinical trial involvement. Due to lack of commercially available ALS biomarkers, alternative strategies to identify and triage patients who likely have ALS must be employed. Several diagnostic tools have been developed to encourage general practitioners to consider ALS and make an urgent referral to ALS specialists, bypassing unnecessary referrals to non-neurologists and unnecessary diagnostic workup.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".