A Predictive Logistic Regression Equation for Neck Pain in Helicopter Aircrew
Bibliographic record
Abstract
INTRODUCTION: While many studies have investigated neck strain in helicopter aircrew, no one study has used a comprehensive approach involving multivariate analysis of questionnaire data in combination with physiological results related to the musculature of the cervical spine. METHODS: There were 40 aircrew members who provided questionnaire results detailing lifetime prevalence of neck pain, flight history, physical fitness results, and physiological variables. Isometric testing data for flexion (Flx), extension (Ext), and right (RFlx) and left (LFlx) lateral flexion of the cervical spine that included maximal voluntary contraction (MVC) force and submaximal exercise at 70% MCV until time-to-fatigue (TTF) was also collected. Muscles responsible for the work performed were monitored with electromyography (EMG) and near-infrared spectroscopy (NIRS) and the associated ratings of perceived exertion (RPE) were collected simultaneously. Results were compiled and analyzed by logistic regression to identify the variables that were predictive of neck pain. RESULTS: While many variables were included in the logistic regression, the final regression equation required two, easy to measure variables. The longest single night vision goggle (NVG) mission (NVGmax; h) combined with the height of the aircrew member in meters (m) provided an accurate logistic regression equation for approximately one-half of our sample (N = 19). Cross-validation of the remaining subjects (N = 21) confirmed this accuracy. CONCLUSION: Our regression equation is simple and can be used by global operational units to provide a cursory assessment without the need for acquiring specialized equipment or training.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".