Risks of radiation versus risks from injury: A clinical decision analysis for the management of penetrating palatal trauma in children
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: Penetrating palatal trauma in children presents a diagnostic dilemma regarding the small but severe risk of injury to carotid vessels. Decisions regarding which children require computed tomography with angiography must be balanced against the risk of radiation-induced malignancy. Our objectives were to compare outcomes between children with and without computed tomography with angiography in the evaluation of palatal trauma and to identify thresholds where the ideal strategy changes in the management of children with palatal trauma through sensitivity analyses. STUDY DESIGN: Decision analytic techniques were used to compare management strategies for penetrating palatal trauma. METHODS: We assigned utilities to the following outcomes: 1) perfect health, 2) future malignancy, 3) carotid injury diagnosed by computed tomography with angiography, and 4) delayed diagnosis of stroke. We calculated outcomes when the risk of stroke ranged from 0.01% to 5.0% for a hypothetical cohort of 10,000 injured children. RESULTS: Not obtaining computed tomography with angiography is the optimal strategy when the stroke risk is less than 4.5%. In two-way sensitivity analyses that consider a range of probabilities of radiation-induced malignancy and stroke, not obtaining computed tomography with angiography on all patients dominates as a strategy until the risk of stroke exceeds 2.3%, and the risk of malignancy is under 0.24%. Routine imaging would introduce 20 additional malignancies for each additional stroke diagnosed. CONCLUSIONS: Routine use of computed tomography with angiography for well-appearing children with palatal trauma should be reconsidered, as the risk of radiation-induced malignancy may outweigh the benefit of identifying the rare carotid injury. LEVEL OF EVIDENCE: 2b.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".