Management of haemothoraces in blunt thoracic trauma: study protocol for a randomised controlled trial
Bibliographic record
Abstract
INTRODUCTION: Haemothorax following blunt thoracic trauma is a common source of morbidity and mortality. The optimal management of moderate to large haemothoraces has yet to be defined. Observational data have suggested that expectant management may be an appropriate strategy in stable patients. This study aims to compare the outcomes of patients with haemothoraces following blunt thoracic trauma treated with either chest drainage or expectant management. METHODS AND ANALYSIS: This is a single-centre, dual-arm randomised controlled trial. Patients presenting with a moderate to large sized haemothorax following blunt thoracic trauma will be assessed for eligibility. Eligible patients will then undergo an informed consent process followed by randomisation to either (1) chest drainage (tube thoracostomy) or (2) expectant management. These groups will be compared for the rate of additional thoracic interventions, major thoracic complications, length of stay and mortality. ETHICS AND DISSEMINATION: This study has been approved by the institution's research ethics board and registered with ClinicalTrials.gov. All eligible participants will provide informed consent prior to randomisation. The results of this study may provide guidance in an area where there remains significant variation between clinicians. The results of this study will be published in peer-reviewed journals and presented at national and international conferences. TRIAL REGISTRATION NUMBER: NCT03050502.
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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 | 0.039 | 0.047 |
| Meta-epidemiology (narrow) | 0.007 | 0.003 |
| Meta-epidemiology (broad) | 0.014 | 0.006 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.096 | 0.014 |
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".