Patient journey to a specialist amyotrophic lateral sclerosis multidisciplinary clinic: an exploratory study
Bibliographic record
Abstract
BACKGROUND: The multidisciplinary approach in the management of Amyotrophic Lateral Sclerosis (ALS) has been shown to provide superior care to devolved care, with better survival, improved quality of care, and quality of life. Access to expert multidisciplinary management should be a standard for patients with ALS. This analysis explores the patient journey from symptom onset and first engagement with health services, to the initial visit to a specialist ALS Multidisciplinary Clinic (MDC) in Dublin, Ireland. METHODS: A retrospective exploratory multi-method study details the patient journey to the MDC. Data from medical interviews and systematic chart review identifies interactions with the health services and key timelines for thirty five new patients presenting with a diagnosis of ALS during a 6 month period in 2013. RESULTS: The time from first symptom to diagnosis was a mean of 16 months (median 13 months), with a mean interval of 19 months (median 14.6) from first symptoms to arrival at the MDC. The majority of patients were seen by a general practitioner, and subsequently by neurology services. There was an average of four contacts with health services and 4.8 investigations/tests, prior to their first Clinic visit. On the first visit to the MDC patients are linked into an integrated 'system' that can provide specialist care and link with voluntary, palliative and community services as required. CONCLUSIONS: Engagement with a multidisciplinary team has implications for service utilization and quality of life of patients and their families. We have demonstrated that barriers exist that delay referral to specialist services. Comprehensive data recording and collection, using multiple data sources can reconstruct the timelines of the patient journey, which can in turn be used to identify pathways that can expedite early referral to specialist services.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".