Patient-based evidence for amyotrophic lateral sclerosis prognostic health communication: “the clock is ticking…how long do I have?”
Bibliographic record
Abstract
Objectives: Prognostic health communication is a critical challenge for amyotrophic lateral sclerosis (ALS) health-care professions, however patient-based evidence for best practice remains limited. We investigated how the experiences of ALS patients and caregivers can inform prognostic communication and whether patient-based evidence supports clinical use of predictive tools. Methods: Data were drawn from ALS Talk, an asynchronous, online focus group study. Patients and family caregivers were recruited from across Canada. Seven groups interacted in a threaded web-forum structure. Sixty-four participants shared experiences and perspectives on prognostic communication. Data were qualitatively analyzed using conventional content analysis and the constant-comparative approach. Results: Primary themes were prognostic communication as an ongoing, evolving conversation; prognostic heterogeneity; progression as an embodiment of prognosis; and functional prognosis. The theme, information needs/wants, contributed to the primary themes. Participants highlighted the importance of stepwise discussions of general and personalized prognosis; prognostic heterogeneity as a source of hope and a potential communication barrier; and how progression facilitates material understanding of prognosis, adaptation, and future planning. Further, participants wanted more information about functional prognosis and the impact of interventions/therapies on function and survival. Conclusions: We discuss participants’ central questions: “how long” and “how well,” and provide recommendations for patient-centred ALS prognostic communication. Participants’ embodied understanding of prognosis and desire for information that anticipates functional change, informs disease management, and facilitates timely planning suggests that clinical application of ALS staging systems may meet patient and caregiver need. Testing in real-world clinical settings is needed to ensure the development of patient-centred predictive tools.
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.
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 | 0.105 | 0.326 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".