Clinical Prediction Models for Suspected Pediatric Foreign Body Aspiration
Bibliographic record
Abstract
Importance: Although various clinical prediction models (CPMs) have been described for diagnosing pediatric foreign body aspiration (FBA), to our knowledge, there is still no consensus regarding indications for bronchoscopy, the criterion standard for identifying airway foreign bodies. Objective: To evaluate currently available CPMs for diagnosing FBA in children. Data Sources: Performed in Ovid MEDLINE, Ovid Embase, PubMed, Web of Science, and CINAHL database with citation searching of retrieved studies. Study Selection: Prediction model derivation and validation studies for diagnosing FBA in children were included. Exclusion criteria included adult studies; studies that included variables that were not available in routine clinical practice and outcomes for FBA were not separate or extractable. Data Extraction and Synthesis: We followed the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies and the Prediction Model Risk of Bias Assessment Tool framework. Data were pooled using a random-effects model. Main Outcomes and Measures: The primary outcome was the diagnosis of FBA as confirmed by bronchoscopy. Characteristics of CPMs and individual predictors were evaluated. The final model presentation with available measures of performance was provided by narrative synthesis. A meta-analysis of individual predictor variables and prediction models was performed. Results: After screening 4233 articles, 7 studies (0.2%; 1577 patients) were included in the final analysis. There were 6 model derivation studies and 1 validation study. Air trapping (odds ratio [OR], 8.3; 95% CI, 4.4-15.5), unilateral reduced air entry (OR, 4.8; 95% CI, 3.5-6.5), witnessed choking (OR, 3.1; 95% CI, 1.0-9.6), wheezing (OR, 2.5; 95% CI, 1.2-5.2), and suspicious findings suggestive of FBA on radiography (OR, 18.5; 95% CI, 5.0-67.7) were the most commonly used predictor variables. Model performance varied, with discrimination scores (C statistic) ranging from 0.74 to 0.88. The pooled weighted C statistic score of all models was 0.86 (95% CI, 0.80-0.92). All studies were deemed to be at high risk of bias, with overfitting of models and lack of validation as the most pertinent concerns. Conclusions and Relevance: This systematic review and meta-analysis suggests that existing CPMs for FBA in children are at a high risk of bias and have not been adequately validated. No current models can be recommended to guide clinical decision-making. Future CPM studies that adhere to recognized standards for development and validation are required.
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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 | 0.051 | 0.174 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.016 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".