Predict Functional Outcome from MSK Network Analysis

Predicts clinical functional outcomes (ROM, strength, composite function score) for injured muscles based on MSK network topology, impact analysis, and optional EMG activation data. Provides evidence-based regression models with confidence intervals.

Usage

mskPredictFunctionalOutcome(
  injured_muscles,
  hg = NULL,
  outcome_type = c("all", "rom", "strength", "function"),
  patient_factors = NULL,
  emg = NULL,
  emg_mapping = NULL,
  confidence_level = 0.95
)

Arguments

injured_muscles

Character or integer vector identifying injured muscles.

hg

An MSKHypergraph object (NULL loads default 173-bone/270-muscle network).

outcome_type

Character: "rom" (range of motion), "strength", "function" (composite functional score), or "all" (default).

patient_factors

Optional list with: age (numeric), sex (character), bmi (numeric), activity_level (character), injury_severity ("mild"/"moderate"/"severe").

emg

Optional EMG data (SummarizedExperiment, matrix, or numeric vector) for activation-based prediction refinement.

emg_mapping

Optional pre-computed data.frame from emgToMSKMapping().

confidence_level

Numeric, confidence level for CIs (default: 0.95).

Value

An S3 object of class "MSKFunctionalOutcome" with:

predictions

data.frame with muscle, outcome_type, predicted_value, lower_ci, upper_ci, unit

aggregate

list with overall_rom, overall_strength, overall_function

recovery_weeks

estimated weeks to reach 90 percent of predicted outcome

confidence_level

numeric

model_type

character

patient_factors_used

list

Clinical Validity

The prediction models are based on MSK network topology and heuristic adjustments. They are not independently validated clinical tools. Use as a research exploration tool, not a clinical diagnostic.

Examples

if (FALSE) { # \dontrun{
outcome <- mskPredictFunctionalOutcome(
  c("Biceps Brachii", "Deltoid"),
  patient_factors = list(age = 55, injury_severity = "moderate")
)
print(outcome)
} # }