Diagnostic delay in amyotrophic lateral sclerosis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it