Physiological Effects of Night Vision Goggle Counterweights on Neck Musculature of Military Helicopter Pilots
Bibliographic record
Abstract
UNLABELLED: Increased helmet-mounted mass and specific neck postures have been found to be a cause of increased muscular activity and stress. However, pilots who use night vision goggles (NVG) frequently use counterweight (CW) equipment such as a lead mass that is attached to the back of the flight helmet to provide balance to counter the weight of the NVG equipment mounted to the front of the flight helmet. It is proposed that this alleviates this stress. However, no study has yet investigated the physiological effects of CW during an extended period of time during which the pilots performed normal operational tasks. METHODS: Thirty-one Canadian Forces pilots were monitored on consecutive days during a day and a NVG mission in a CH-146 flight simulator. Near infrared spectroscopy probes were attached bilaterally to the trapezius muscles and hemodynamics, i.e., total oxygenation index, total hemoglobin, oxyhemoglobin, and deoxyhemoglobin, were monitored for the duration of the mission. Pilots either wore CW (n = 25) or did not wear counterweights (nCW, n = 6) as per their usual operational practice. RESULTS: Levene's statistical tests were conducted to test for homogeneity and only total oxygenation index returned a significant result (p < or = 0.05). For the near infrared spectroscopy variables, significant differences were found to exist between CW and nCW pilots for total hemoglobin, deoxyhemoglobin, and oxyhemoglobin during NVG flights. The CW pilots displayed less metabolic and hemodynamic stress during simulated missions as compared to the nCW pilots. CONCLUSION: The results of this study would suggest that the use of CW equipment during NVG missions in military helicopter pilots does minimize the metabolic and hemodynamic responses of the trapezius muscles.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".