Ethical considerations in conducting surgical research in severe complicated intra-abdominal sepsis
Bibliographic record
Abstract
Background: Severe complicated intra-abdominal sepsis (SCIAS) has high mortality, thought due in part to progressive bio-mediator generation, systemic inflammation, and multiple organ failure. Treatment includes early antibiotics and operative source control. At surgery, open abdomen management with negative-peritoneal-pressure therapy (NPPT) has been hypothesized to mitigate MOF and death, although clinical equipoise for this operative approach exists. The Closed or Open after Laparotomy (COOL) study (https://clinicaltrials.gov/ct2/show/NCT03163095) will prospectively randomize eligible patients intra-operatively to formal abdominal closure or OA with NPTT. We review the ethical basis for conducting research in SCIAS. Main body: Research in critically ill incapacitated patients is important to advance care. Conducting research among SCIAS is complicated due to the severity of illness including delirium, need for emergent interventions, diagnostic criteria confirmed only at laparotomy, and obtundation from anaesthesia. In other circumstances involving critically ill patients, clinical experts have worked closely with ethicists to apply principles that balance the rights of patients whilst simultaneously permitting inclusion in research. In Canada, the Tri-Council Policy Statement-2 (TCPS-2) describes six criteria that permit study enrollment and randomization in such situations: (a) serious threat to the prospective participant requires immediate intervention; (b) either no standard efficacious care exists or the research offers realistic possibility of direct benefit; (c) risks are not greater than that involved in standard care or are clearly justified by prospect for direct benefits; (d) prospective participant is unconscious or lacks capacity to understand the complexities of the research; (e) third-party authorization cannot be secured in sufficient time; and (f) no relevant prior directives are known to exist that preclude participation. TCPS-2 criteria are in principle not dissimilar to other (inter)national criteria. The COOL study will use waiver of consent to initiate enrollment and randomization, followed by surrogate or proxy consent, and finally delayed informed consent in subjects that survive and regain capacity. Conclusions: A delayed consent mechanism is a practical and ethical solution to challenges in research in SCIAS. The ultimate goal of consent is to balance respect for patient participants and to permit participation in new trials with a reasonable opportunity for improved outcome and minimal risk of harm.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.038 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 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; both teacher heads agree on what is shown here.
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".