Holistic View of Autografting Patients by Percentage of Total Body Surface Area Burned: Medical Record Abstraction Integrated with Administrative Claims
Bibliographic record
Abstract
Aim: This retrospective observational study provides a holistic view of the clinical and economic characteristics of inpatient treatment of patients with thermal burns undergoing autografting, by integrating real-world data (RWD) from medical records from healthcare providers (HCPs) and administrative claims. Methods: ) and obtained their medical records from HCPs. We abstracted data from medical records to describe patient demographics and clinical characteristics and obtained costs of treatment from claims. Results: Two hundred patients were stratified into cohorts based on the percentage of total body surface area (%TBSA) burned: minor (< 10%), moderate (10%-24%), and major (≥ 25%). Data obtained from medical records and administrative claims were comparable to previous findings from administrative claims data. This privately insured study cohort predominantly consisted of White men. Diabetes mellitus and hypertension were frequently reported in a relatively young population. Key clinical characteristics that could influence burn treatment decisions and long-term outcomes, such as body mass index, size of autograft donor site, and mesh ratio, were frequently underdocumented in patients' medical records. Conclusion: Evidence generated from 2 orthogonal RWD sources confirmed that patients with larger %TBSA burned required more intensive care, thereby incurring higher costs. This study highlights considerable incompleteness in many critical fields in medical records, which limits the ability to generate broader insights. More comprehensive documentation of clinical characteristics and outcomes of autografts and donor sites in the operative and medical notes is critical to appropriately evaluate their impact on outcomes of burn treatments in future research using RWD.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".