Next of kin’s Reactions to Results of Functional Neurodiagnostics of Disorders of Consciousness: a Question of Information Delivery or of Differing Epistemic Beliefs?
Bibliographic record
Abstract
Abstract Our recent publication in Neuroethics re-constructed the perspectives of family caregivers of patients with disorders of consciousness (DOC) on functional neurodiagnostics (Schembs et al., Neuroethics, 2020). Two papers criticized some of our methodological decisions (Peterson, Neuroethics, 2020; Andersen et al., Neuroethics, 2020) and commented on some conclusions. In this commentary, we would like to further explain our methodological decisions. Despite the limitations of our findings, which we readily acknowledged, we continue to think they entail valid hypotheses that need further investigation. We conclude that some caregivers with high hopes for the recovery of their loved ones with DOC will most likely not consider results of functional neuroimaging as guiding information for treatment decisions, despite efforts taken to deliver information to them. Caregivers of that type might argue that such test-results are not a reliable source of information for the judgement of whether their loved one is likely going to recover or not (prognosis). We introduce the concept of epistemic beliefs to formulate this hypothesis and suggest that future qualitative studies in this area should be aware of such beliefs when investigating the effects of functional neurodiagnostics on knowledge communication and shared decision making for patients with DOC.
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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 | 0.124 | 0.446 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".