How to break the news in amyotrophic lateral sclerosis/motor neuron disease: practical guidelines from experts
Bibliographic record
Abstract
In amyotrophic lateral sclerosis/motor neuron disease (ALS/MND), it is necessary to communicate difficult news during the initial diagnosis and throughout the disease trajectory as the condition progresses. However, delivering difficult news to people with ALS/MND is an emotionally demanding task for healthcare and allied health professionals—one for which many feel ill-prepared because of limited training in this area. Ineffective communication of difficult news damages the patient-provider relationship and negatively impacts patient quality of life (QoL). To address this issue, we developed the A-L S-PIKES protocol based on available literature and our extensive clinical experience. It provides easy-to-follow, stepwise guidelines to effectively deliver difficult news to people with ALS/MND (PALS) that includes: Advance Preparation (preparing for the discussion logistically and emotionally); Location & Setting (creating a comfortable setting that fosters rapport); Patient’s Perceptions (assessing PALS’ understanding and perception of their condition); Invitation (seeking PALS’ permission to share information); Knowledge (sharing information in a clear, understandable manner); Emotion/Empathy (addressing emotions with empathy and providing emotional support); and Strategy & Summary (summarizing the discussion and collaboratively developing a plan of action). A–L S–PIKES provides practical guidelines on how to prepare for and conduct these challenging conversations. It emphasizes effective communication tailored to the individual needs of PALS and their families, empathy, sensitivity, and support for PALS’ emotional well-being and autonomy. The aim of A-L S-PIKES is to both enhance skills and confidence in delivering difficult news and to improve the QoL of PALS and their families. Future studies should systematically evaluate the feasibility and effectiveness of A-L S-PIKES to establish its utility in clinical practice.
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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 | 0.028 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.014 | 0.014 |
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".