A protocol for a scoping and qualitative study to identify and evaluate indications for damage control surgery and damage control interventions in civilian trauma patients
Bibliographic record
Abstract
INTRODUCTION: Initial abbreviated surgery with planned reoperation (damage control surgery) is frequently used for major trauma patients to rapidly control haemorrhage while limiting surgical stress. Although damage control surgery may decrease mortality risk among the severely injured, it may also be associated with several complications when inappropriately applied. We seek to scope the literature on trauma damage control surgery, identify its proposed indications, map and clarify their definitions, and examine the content and evidence on which they are based. We also seek to generate a comprehensive list of unique indications to inform an appropriateness rating process. METHODS AND ANALYSIS: We will search 11 electronic bibliographic databases, included article bibliographies and grey literature sources for citations involving civilian trauma patients that proposed one or more indications for damage control surgery or a damage control intervention. Indications will be classified into a predefined conceptual framework and categorised and described using qualitative content analysis. Constant comparative methodology will be used to create, modify and test codes describing principal findings or injuries (eg, bilobar liver injury) and associated decision variables (eg, coagulopathy) that comprise the reported indications. After a unique list of codes have been developed, we will use the organisational system recommended by the RAND/University of California, Los Angeles (RAND-UCLA) Appropriateness Rating Method to group principal findings or injuries into chapters (subdivided by associated decision variables) according to broader clinical findings encountered during surgical practice (eg, major liver injury). ETHICS AND DISSEMINATION: This study will constitute the first step in a multistep research programme aimed at developing appropriate, evidence-informed indications for damage control in civilian trauma patients. With use of an integrated knowledge translation intervention that includes collaboration with surgical practice leaders, this research may allow for development of indications that are more likely to be relevant to and used by surgeons. Ethics approval is not required for this study.
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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 | 0.146 | 0.121 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.105 | 0.022 |
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".