Face, Content, and Construct Validity of Brain Tumor Microsurgery Simulation Using a Human Placenta Model
Bibliographic record
Abstract
BACKGROUND: Brain tumors are complex 3-dimensional lesions. Their resection involves training and the use of the multiple microsurgical techniques available for removal. Simulation models, with haptic and visual realism, may be useful for improving the bimanual technical skills of neurosurgical residents and neurosurgeons, potentially decreasing surgical errors and thus improving patient outcomes. OBJECTIVE: To describe and assess an ex vivo placental model for brain tumor microsurgery using a simulation tool in neurosurgical psychomotor teaching and assessment. METHODS: Sixteen human placentas were used in this research project. Intravascular blood remnants were removed by continuous saline solution irrigation of the 2 placental arteries and placental vein. Brain tumors were simulated using silicone injections in the placental stroma. Eight neurosurgeons and 8 neurosurgical residents carried out the resection of simulated tumors using the same surgical instruments and bimanual microsurgical techniques used to perform human brain tumor operations. Face and content validity was assessed using a subjective evaluation based on a 5-point Likert scale. Construct validity was assessed by analyzing the surgical performance of the neurosurgeon and resident groups. RESULTS: The placenta model simulated brain tumor surgical procedures with high fidelity. Results showed face and content validity. Construct validity was demonstrated by statistically different surgical performances among the evaluated groups. CONCLUSION: Human placentas are useful haptic models to simulate brain tumor microsurgical removal. Results using this model demonstrate face, content, and construct validity.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".