Convergent 3-6 kHz cochlear vulnerability in aircrew: matching threshold sound conditioning targets to operational flight profiles.

Commercial and military aircrew operate within a multi-hazard auditory environment that combines cabin acoustic noise, chronic hypobaric hypoxia under regulatory cabin-pressure caps, and altitude-dependent cosmic ionizing radiation. Their convergent action on cochlear vulnerability has not been quantified, and no decision framework exists for matching threshold sound conditioning (TSC) target frequencies to operational flight profiles. This work is a computational, hypothesis-generating framework rather than an empirical study. We propose that the dominant frequency band of cochlear vulnerability in aircrew (3-6 kHz) emerges as the geometric overlap of three mechanism-distinct vulnerability kernels and coincides with the TSC selection region (3, 4 or 6 kHz) evaluated in the Kwak and Kwak 2020 randomized controlled trial. We construct a Cumulative Cochlear Stress Index (CCSI) that combines a CARI-7-derived radiation dose formula, an estimated-alveolar-tension hypoxia term and an A-weighted noise term, weighted by frequency-resolved Gaussian vulnerability kernels in log-octave space. The geometric mean of the three kernels peaks at f * = 4.23 kHz (FWHM 2.06-8.72 kHz) and is consistent with the convergence hypothesis on the discrete clinical TSC grid (argmax 4 kHz). A sensitivity analysis over the full audiometric grid {0.5, 1, 2, 3, 4, 6, 8, 12} kHz, six mixing-weight scenarios (including a radiation-excluded w R = 0 case) and four aircrew profiles finds the global CCSI argmax inside 3/4/6 kHz in all 24 combinations, and a Monte-Carlo randomization of the kernel parameters within literature bounds reproduces the 4 kHz convergence in 99.2% of draws, vs. 23.1% for unconstrained kernels, a non-tautological robustness check. Acoustic noise and hypobaric hypoxia are treated as the primary, mechanistically supported axes, while cosmic ionizing radiation is retained as an exploratory third axis given the absence of direct cochlear evidence at cruise-altitude doses. A machine-learning identifiability check (Random Forest, Gradient Boosting and Ridge ensembles on a 500-profile synthetic dataset; band-averaged R 2≈0.98) is reported as an internal model-recovery check rather than a biological validation. The framework supports prospective evaluation of TSC in aircrew cohorts.