HEAD TURNING SIGN FOR DIAGNOSIS OF DEMENTIA AND MILD COGNITIVE IMPAIRMENT: A REVALIDATION
Bibliographic record
Abstract
Objective To examine the utility of the head turning sign in the diagnosis of dementia and mild cognitive impairment, and to compare results with a previous report from this clinic (J Neurol Neurosurg Psychiatry 2012;83:852–3). Methods/Setting Prospective observational study, Cognitive Function Clinic. Results Of 191 consecutive new outpatients (M:F=100:91; age range 20–89 years, median 60 years) seen over a 10–month period (February–December 2012), 85 had cognitive impairment (55 with dementia by DSM–IV–TR criteria, 30 with MCI). Considering the whole cohort, presence of the head turning sign (HTS+) had sensitivity 0.61 and specificity 0.98 for the diagnosis of cognitive impairment; these figures were comparable to those (0.60, 0.98 respectively) from the previously examined cohort (January–October 2011). Considering only those patients who attended with an informant (n=113), HTS+ had sensitivity 0.68 and specificity 0.94 for diagnosis of cognitive impairment, again comparable with the previous cohort (0.63, 0.95 respectively). Positive predictive values were high (>0.9) in both cohorts. Conclusions This study confirms that the head turning sign is very specific but not very sensitive for the diagnosis of cognitive impairment in an unselected cognitive clinic cohort, with a high positive predictive value.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".