Simulated malingering in the testing of cervical muscle isometric strength
Bibliographic record
Abstract
We sought to determine if simulated malingering trials of isometric cervical muscular strength in flexion, extension and right/left bending are substantially different from maximum effort trials in young, healthy subjects. A convenience sample of healthy, young adult subjects was used (M=9, F=9) who were free of neck pain. A uniaxial load cell was used to measure forces (N) produced by three trials of isometric flexion, extension and bilateral bending contractions of the head/neck muscles in two modes: comfortable maximum (MAX) and simulated (insincere) malingering (INSIN). An ANOVA model was created and tested post-hoc for paired differences within and between modes and genders. A separate ANOVA was conducted to test for differences in the ratio between flexion and extension (F/E ratio). In MAX mode, males were stronger in all ranges vs females; the expected F/E and bilateral ratios were demonstrated and good consistency of effort within and between trials was demonstrated by low CV's and high ICC's, respectively. In INSIN mode, all mean peak values were significantly lower in both genders; however, the difference between genders disappeared. Within-trial consistency was much poorer with significantly higher CV's while between-trial variability was good as demonstrated by high ICC's. The flexion/extension ratio was increased in INSIN vs MAX, with no difference between genders. It appears that simulated malingering trials produced consistent patterns of deviation from maximal effort trials: reduced peak values, increased flexion/extension ratio and increased variability of within-trial effort. These findings may provide a basis for valid indicators of insincere effort in neck pain 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".