Enhancing trauma laparotomy patient outcomes: Comprehensive approaches to care improvement
Bibliographic record
Abstract
This thesis aims to enhance patient care for trauma laparotomy patients by focusing specifically on hospital length of stay (HLOS), a critical metric in resource-constrained healthcare systems. Although trauma laparotomy patients represent a relatively homogenous trauma cohort, they exhibit diverse needs and varied outcomes, necessitating targeted interventions. A series of studies was conducted to explore strategies, particularly the implementation of enhanced recovery protocols (ERPs), to optimize HLOS and improve patient care. Chapter 2 analyzed data from 27,434 trauma laparotomy patients in the National Trauma Data Bank, a large trauma registry in the United States, to provide a clearer understanding of this patient population. The study found an overall median HLOS of 7.0 days, with 77% of patients having an HLOS of less than 11 days, indicating the potential applicability of ERPs for this population. Factors associated with HLOS, when stratified by length of stay, included injury type, complications, comorbidities, and insurance status, underscoring the value of this approach for targeted interventions.Chapter 3 examined unnecessary hospital stays, a distinct type of prolonged HLOS that is particularly impactful in Canada’s universal healthcare system. A retrospective analysis at Montreal General Hospital revealed that approximately 30% of trauma laparotomy patients experienced unnecessary stays, resulting in 513 additional hospital days during the study period. Delays were primarily due to limited availability in rehabilitation (42.2%) and psychiatric department (39.1%). These insights suggest potential interventions, such as improved access to post-acute care and enhanced inter-departmental coordination, to optimize resource efficiency. Chapter 4 details the development and implementation of the Trauma Laparotomy Care Pathway (TLCP), an ERP specifically tailored for trauma laparotomy patients, followed by a prospective pilot study assessing adherence to pathway components and its impact on outcomes. A comprehensive literature review, covering both trauma laparotomy and emergency abdominal surgery due to the limited number of ERP studies specific to trauma laparotomy, was conducted as a foundation for TLCP development. The review identified 39 studies, highlighting an increase in ERP research over the past decade. However, only three studies to date have focused exclusively on trauma laparotomy, and none were conducted in North America, indicating an opportunity for ERP implementation in this context. A consensus-based TLCP was developed and implemented in our clinical setting. In the first six months post-implementation, adherence to pathway components ranged from 54.5% to 67.7%, and TLCP reduced HLOS by two days compared to the historical cohort (4.0 days [3.5, 6.5] vs 6.0 days [4.0, 10.0], p=0.0021) without an increase in complications or readmissions. In conclusion, stratifying trauma laparotomy patients by HLOS effectively identifies subgroups with distinct characteristics and healthcare needs, highlighting the importance of targeted interventions. The newly developed TLCP can be applied to select trauma laparotomy patients, offering the potential for improved outcomes. Addressing factors contributing to unnecessary stays, along with pathway use, may further enhance patient outcomes. These efforts represent initial steps toward improving care for trauma laparotomy patients on a larger scale
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.006 |
| 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".