The Diagnostic Peritoneal Lavage: A Brief Review of the Current Literature and an Analysis of Its use and Teaching in Canada
Bibliographic record
Abstract
ABSTRACT Introduction The vast majority of the published data on the appropriate use of the diagnostic peritoneal lavage (DPL) is dated. Currently in Canada, there is a significant grey-zone surrounding the teaching and use of the DPL. The objectives of this article are to briefly review the recent literature, to analyze the patterns in the use of the DPL in Canada, and to determine whether general surgery residents are being taught the skill. Methods Literature review was conducted using PubMed and Ovid. The Canadian Institute for Health Information (CIHI) supplied the available data on DPL usage in Canada. General surgery program directors in Canada were contacted by e-mail to determine if the DPL was still being taught. Results Between the years of 2001 and 2006 in Canada, 38 DPLs were coded into the CIHI's database. Males accounted for 84.2% of the DPLs, and 23.7% of all DPLs resulted in a laparotomy. Motor vehicle crashes were the reason that 42% of the DPLs were performed. The majority of general surgery residency programs in Canada are not teaching the DPL outside of what is taught in the advanced trauma life support (ATLS) course. Discussion The literature reviewed shows that the DPL continues to play a role in the current management of patients suffering from both blunt and penetrating trauma, although a much more limited role than in the past. How to cite this article Boone D, Clarkson CA. The Diagnostic Peritoneal Lavage: A Brief Review of the Current Literature and an Analysis of Its use and Teaching in Canada. Panam J Trauma Critical Care Emerg Surg 2012;1(3):150-153.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.028 | 0.056 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".