Tests of Motor Function in Patients Suspected of Having Mild Unilateral Cerebral Lesions
Bibliographic record
Abstract
OBJECTIVE: Though various textbooks describe clinical manoeuvres that help detect subtle motor deficits, their sensitivity, specificity and predictive values have not been determined. We investigated the sensitivity, specificity and predictive values of various manoeuvres in order to determine the most sensitive and reliable test or combination thereof. METHODS: Straight arm raising (Barré), pronator drift, Mingazzini's manoeuvre, finger tap, forearm roll, segmental strength and deep tendon reflexes were tested in 170 patients with (86) and without (84) a proven lesion in the motor areas confirmed by computed tomography. RESULTS: Segmental motor strength bad good specificity (97.5%) but poor sensitivity (38.9%) and negative predictive value (NPV) (58.7%). The forearm roll had a similar profile. Finger tap had a sensitivity of 73.3% and a specificity of 87.5%. Barré and pronator testing had a sensitivity and specificity of 92.2% and 90.0% respectively. Hyperreflexia had a sensitivity of 68.9% and a specificity of 87.5%. An abnormality of pronator, reflexes or finger tap had a sensitivity of 97%, and when these three tests were positive, specificity was 97%. When all six tests were positive, the positive predictive value was 100%, when all six tests were negative the NPV was 100%. CONCLUSION: The detailed segmental examination has very good specificity for detecting motor deficits, but the sensitivity and NPV are unacceptably low. Pronator drift with finger tap and reflexes is the most reliable and time-effective combination of tests for the detection of subtle motor lesions, and could replace the segmental motor examination as a screening for motor lesions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".