Simulation Tests for Cervical Nonorganic Signs: A Study of Face Validity
Bibliographic record
Abstract
OBJECTIVES: The purpose of this study was to develop and determine the face validity of additional cervical nonorganic simulation tests. METHODS: Four simulation tests were either selected from the literature or newly designed: simulated sitting trunk/shoulder rotation (SR; test no. 1), active vs passive cervical rotation (CR; test no. 2), Libman's test (LT; test no. 3) of pressure over the mastoid process, and side-lying passive shoulder abduction (SA; test no. 4). Three groups, 1 without neck pain (n = 44) and 2 with neck pain (n = 43 and 27), were formed. Outcome measures consisted of questions on provocation of pain (Yes/No) and appropriateness (Yes/No) as well as measurements of cervical rotation (goniometric) and pressure pain threshold (pressure algometer). Group test responses were evaluated and scored. A threshold of acceptance was established at 80% agreement for face validity. Ranges of rotation and pressure threshold values were analyzed with the Student t test. RESULTS: In nonneck pain subjects, all 4 tests were rated as nonpainful and 3 were rated as "appropriate" for neck pain examination (not SR). In neck pain subjects, this test and SA were rated as nonpainful, whereas LT was rated as painful in 26% of subjects. Only CR and LT were rated as "appropriate." In neck pain subjects, passive rotations exceeded actives by 10% to 14% (P = .000). On a second round of testing with a slightly modified method, SR and SA achieved acceptable "appropriateness." CONCLUSIONS: Once 2 tests were slightly modified, all 4 tests were found to have acceptable face validity. Further research into the reliability of these tests as well as into the combinations of these tests is warranted.
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 | 0.017 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".