Casualties
The model draws from a large patient-level cohort. Rows carry mechanism, severity, body-region pattern, mobility, surgical need, blood demand, and mortality-risk fields that stay internally consistent.
Build and audit combat casualty flow scenarios across gridded force layouts, role-of-care assets, evacuation policies, and surgical capacity constraints.
BattlefieldMed is a research simulator for asking how combat casualty volume, evacuation, blood, surgeons, beds, and routing decisions interact during large-scale combat operations. It is built to make assumptions visible, stress-test them, and show where a medical force structure starts to fail.
synthetic modeled casualties
consolidated sources in the model source library, with hundreds more screened
injury-relative follow-up for mortality, disposition, resource use, and audit checks
The model draws from a large patient-level cohort. Rows carry mechanism, severity, body-region pattern, mobility, surgical need, blood demand, and mortality-risk fields that stay internally consistent.
Users set casualty density, battle templates, operation phases, main effort, battalion origins, and echelon distances on a simplified planning grid.
The workbench represents Role I, Role II, and Role III nodes; OR tables; surgeon specialties; ICU and holding capacity; post-op decompression pressure; and work-rest limits.
Ground and air assets move casualties through the network. Routing policies can keep patients on primary paths, bypass echelons, or react to queues and available capability.
Casualty rows generate blood need, pre-Role II walking blood bank demand, surgical specialty demand, triage priority, and downstream critical-care burden.
Runs report time to surgery, CFR, DOW, queueing, utilization, blood use, cause of death, and assumption audits. Monte Carlo mode repeats scenarios to show uncertainty instead of one lucky run.
No direct dataset exists for a modern military casualty care system absorbing the volume, saturation, and delays expected in large-scale combat operations. BattlefieldMed therefore extrapolates from the most applicable modern combat and trauma data, combines those anchors with historical mass-casualty experience, and infers physiologically plausible casualty behavior under delayed care. When configured for GWOT-comparable conditions, the system is checked against observed GWOT performance so those inference bridges stay visible.
This page is not a full calibration catalog. It shows how selected empirical anchors constrain the model: modern combat-care performance, early trauma mortality timing, 30-day survival shape, delayed-care physiology, and historical-scale casualty volume.
BattlefieldMed cannot be calibrated against a single observed LSCO outcome dataset, because that dataset does not exist. Instead, selected model behaviors are compared directly against the best available empirical anchors: Shackelford et al. for combat mortality timing and surgical-delay effect size, and April et al. for resource-limited survival behavior during prolonged trauma care.
Combat casualties alive at evacuation request; 30-day mortality timing and 24-hour surgical-delay association.
| Measure | Reported Data | Simulator Calibration | Alignment |
|---|
Resource-limited South African non-compressible torso hemorrhage cohort; 24-hour survival conditional on survival time.
| Measure | Reported Data | Simulator Calibration | Alignment |
|---|
August 5 AM density-1 comparator run using the GWOT 25k cohort, Role I at point of injury, permissive forward air access, and shortened evacuation geometry.
| Measure | Reported GWOT Comparator | Simulator Calibration Run | Readout |
|---|
| DOW Boundary Test | Current Role I Boundary | Role II Boundary | Surgery-Start Boundary |
|---|
The close matches shown here are intentional calibration targets and comparator checks, not post hoc curve fitting to every possible output. Shackelford directly anchors early death timing and the central surgical-delay hazard effect. April anchors the magnitude and timing of resource-limited deterioration, especially the importance of the first 3 to 4 hours. The GWOT check asks whether the model returns plausible modern-system CFR and DOW behavior when configured for GWOT-like density, geometry, and evacuation access. Remaining LSCO behavior is extrapolated from these anchors plus the simulated casualty phenotypes, queues, blood demand, and available surgical capability.
Select a saved or built-in version.
Applies to surgeon calendars after case completion. This changes effective surgical availability, not casualty generation or routing.
Forward echelon evacuation leg
Rearward decompression leg
Active behavior: random cohort draw. The phenotype row supplies ISS, surgery type, mobility, blood demand, and mortality clocks.
This remains visible for calibration, but the newer design treats rapid deaths as first-minutes phenotype clocks rather than a separate hidden mortality system.
65% litter
85% litter
95% litter
100% litter
35% litter
5% litter
These rows override the broad nonsurgical defaults for combat-trauma patients after their nonoperative ISS bucket is assigned. DNBI patients still use the clinical-domain defaults.
2% litter
15% litter
75% litter
100% litter
20% litter
45% litter
80% litter
100% litter
Derived donor pool
Forward availability accounting records source and shortfall without changing evacuation, surgery, or survival.
These controls shape regenerated/live-generator blood demand. The default sampled cohort already carries WBE need on each casualty row.
This mirrors rapid pre-MTF KIA on the casualty page. These casualties do not consume workload, but their cause and early timing remain visible in cohort clock audits and all-casualty survival curves.
DNBI stays combined for now. These casualties still enter litter/ambulatory assignment and triage; they do not receive the general/trauma or neurosurgical unmet-capability penalty unless generated as a true surgical NBI case.
Use the preview slider as a what-if: moving it right shows later access to needed trauma or neurosurgical care and should pull the modified survival curves downward, especially for severe and critical casualties. Actual DES runs use each casualty's simulated wait time. Early hazard is limited to the configured early window, prolonged unmet need adds capped hourly risk after the grace point, and delayed survivors can retain capped post-op debt. Isolated orthopedic-only cases do not receive this mortality penalty.
Adaptive convergence is always evaluated independently for each scenario cell: scenario, density, routing policy, and selected comparison.
This seed reproduces the whole run package. Each scenario cell and iteration receives its own logged seed derived from it.
The workbook and CSV files contain the complete audit/output detail. These tables are kept here only as a quick preview.