Lies, damned lies and diagnoses: Estimating the clinical utility of assessments of covert awareness in the vegetative state
Bibliographic record
Abstract
BACKGROUND: Functional neuroimaging of patients in the vegetative state has been shown to provide diagnostic and prognostic information beyond that which conventional behavioural assessments may allow. However, before these promising approaches may reach large numbers of patients through a standard clinical protocol, it is necessary to determine the utility of these assessments-i.e. the accuracy of their diagnoses. METHODS AND RESULTS: This study demonstrated that, due to the nature of statistical testing and the absence of a 'ground truth' of consciousness, it is impossible to calculate the conventional measures of clinical utility-sensitivity and specificity-for diagnoses made on the basis of functional neuroimaging for command-following. Nevertheless, it is crucial for such measures to be determined in order for valuable clinical resources to be distributed wisely. Therefore, a number of alternative guidelines are offered for the estimation of clinical utility. CONCLUSIONS: By evaluating new and existing functional neuroimaging methods against the proposed guidelines, this study argues that it may be possible to achieve dramatically and efficiently improved diagnostic and prognostic accuracy for all vegetative state patients.
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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 | 0.014 | 0.113 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".