Challenges and opportunities for expediting ALS diagnosis in Alberta, Canada: a human-centred design approach
Bibliographic record
Abstract
INTRODUCTION: Amyotrophic lateral sclerosis (ALS) is a rapidly progressive, fatal motor neuron disease. Diagnostic delay severely impairs patient access to ALS multidisciplinary clinics, available disease-modifying medications and therapies that may prolong survival. OBJECTIVES: To investigate how patient and physician perspectives might be leveraged to promote timely ALS diagnosis, and how system-level barriers might be addressed to promote appropriate referral to ALS multidisciplinary care. DESIGN AND SETTING: A qualitative study in Alberta, Canada, used human-centred design and interviews to map the diagnostic journeys of ALS patients and identify individual-level and system-level diagnostic barriers and opportunities. PARTICIPANTS AND ANALYSIS: 30 semistructured interviews (10 patients; 20 physicians) were conducted. Data were inductively analysed with the aid of Miro board software. Patient and physician data were triangulated to identify key phases of the journey from symptom onset to confirmed ALS diagnosis and themes related to the diagnostic barriers and opportunities. Journey maps were created to visualise the diagnostic journey. RESULTS: Patient journeys were comprised of five phases and commonly involved iterative cycles of referral and testing before an ALS diagnosis was confirmed. Four primary themes related to diagnostic barriers: difficulty recognising and responding effectively to early-stage ALS symptoms, absence of a single definitive diagnostic test, long wait times between referrals and clinical visits, and physician reluctance to pronounce an ALS diagnosis. Analysis indicated three approaches for improving diagnostic processes: increase ALS awareness; improve communication between referring physicians and physicians receiving referrals (consultants); and develop physician, diagnostic testing and multidisciplinary clinic referral forms that will guide symptom assessment and accurate referral. CONCLUSIONS: Timely ALS diagnosis is challenging for patients navigating the frequently prolonged, circuitous diagnostic journey and physicians who struggle with referral pathways and the efficient diagnosis of this rare disease. Findings demonstrate the importance of increased ALS awareness and effective communication and response within referral pathways. Recommendations include strengthening the clinical approach of community-based physicians and supporting access and referral pathways. Current initiatives arising from this investigation seek to achieve meaningful change in timely referrals for progressive neurological diseases like ALS.
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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 | 0.042 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".