Breaking bad news in amyotrophic lateral sclerosis: The need for medical education
Bibliographic record
Abstract
The manner in which physicians deliver difficult diagnoses is an area of discontent for patients with amyotrophic lateral sclerosis (ALS). The American Academy of Neurology's Practice Parameter for care of the ALS Patient recommended teaching and evaluating strategies for disclosing the diagnosis (10). Our objective was to examine residents' ability in and perceptions of communicating the diagnosis of ALS. Twenty-two resident physicians were videotaped and rated by two ALS neurologists as they delivered an ALS diagnosis to a standardized patient (SP) during an objective structured clinical examination (OSCE). Residents self-rated immediately after the OSCE, again after viewing their videotape, and completed a survey regarding the OSCE and delivering difficult diagnoses. OSCE performance was suboptimal, particularly for communication skills and empathy. The two examiners' scores correlated except for the empathy subscore. Residents' self-assessments did not align with the examiners' scores either before or after watching their videotape. The survey uncovered residents' apprehension and dissatisfaction with their training in diagnosis delivery. The results highlight a need for resident education in delivering an ALS diagnosis. The lack of correlation between residents' and examiners' scoring requires further study. Evaluation of empathy is particularly challenging. Residents agreed that OSCE participation was worthwhile.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".