Identifying Pediatric Trauma Data Gaps at a Large Urban Trauma Referral Center in Santiago, Chile
Bibliographic record
Abstract
ABSTRACT Background Trauma registries contribute to improving trauma care, but their impact is highly dependent on the quality of the data. A simplified point of care pediatric trauma registry (PTR) was developed at the Centre for Global Surgery from the McGill University Health Centre (MUHC) for implementation in Low-middle income countries (LMICs). Pilot deployment was launched at a large urban trauma center in May 2016 in Santiago, Chile. Prior to deployment, we sought to identify missing data in existing trauma records in order to optimize PTR practicality and user benefit. Materials and methods The project was approved by the local Institutional Review Board. Retrospective chart review was conducted on trauma patients below the age of 15 who were evaluated at the emergency room (ER) of Hospital Dr. Sotero del Rio (HSR) between January 1st and June 30th 2015. Data missingness was evaluated for each component of the PTR (demographics, mechanism, injury and outcomes). Potential independent predictors of data missingness were evaluated using multiple linear regression. Results A total of 351 patients were included. Demographic data missingness ranged from 0% (age) to 95% (mode of arrival). Mechanism data missingness ranged from 6% (cause of injury) to 42% (site of injury). Injury physiology data missingness ranged from 37% (oxygen saturation) to 99% (respiratory rate). Interestingly, mean injury anatomy data missingness was significantly inferior to physiology data (0.6% vs. 78.6%, p < 0.05). Outcome data missingness reached 54% at 2 weeks. Conclusion In resource-limited settings, high quality data is essential to guide responsible resource allocation. We believe implementation of a simplified trauma registry has the potential to reduce data gaps for pediatric trauma patients by streamlining trauma data collection at point of care. This should include streamlined data collection with a short per-patient completion time, and should forego attempts to collect data at 2 weeks, which has proven unsuccessful. How to cite this article St-Louis E, Roizblatt D, Deckelbaum DL, Baird R, Millán CV, Ebensperger A, Razek T. Identifying Pediatric Trauma Data Gaps at a Large Urban Trauma Referral Center in Santiago, Chile. Panam J Trauma Crit Care Emerg Surg 2017;6(3):169-176.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".