“Likelihood to be diagnosed or misdiagnosed” revisited: the Efficiency Index
Bibliographic record
Abstract
Objectives: To calculate an “efficiency index” (EI) based on “numbers needed” metrics (specifically to diagnose and to misdiagnose) for cognitive screening instruments which are commonly used in suspected dementia, and to compare these values with those of a previously described “likelihood to be diagnosed or misdiagnosed” (LDM) metric also based on “numbers needed” metrics. Methods: Datasets from pragmatic test accuracy studies examining four brief cognitive screening instruments (Mini-Mental State Examination, MMSE; Montreal Cognitive Assessment, MoCA; Mini-Addenbrooke’s Cognitive Examination, MACE; Free-Cog) were analysed to calculate values for EI and LDM, and to examine their variation with test cut-off for MACE,. Findings: EI and LDM had similar values for all the screening instruments examined but EI was simpler to calculate than LDM. Both metrics showed similar maxima across a range of cut-offs for MACE. The differing score range of EI and LDM allowed the former, but not the latter, to be qualitatively classified as for likelihood ratios, with three of the tests (MMSE, MACE, Free-Cog) achieving a “moderate” increase in the likelihood of dementia diagnosis. Conclusions: The efficiency index (EI) metric indicates the utility or inutility of diagnostic tests in a way that is easily intelligible for both clinicians and patients, illustrating the inevitable trade-off between diagnosis and misdiagnosis.
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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 | 0.059 | 0.249 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| 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".