Expediting operational damage control laparotomy closure : iTClampTM vs suturing during damage control surgical simulation training
Bibliographic record
Abstract
Background: As recent innovations in informatic technology allow for specialist support to point-of-care (POC) providers, advanced interventions, such as damage control surgery (DCS), may be possible on the front lines. However, for non-surgeons suturing is a very complex procedural skill, and thus other operational laparotomy closure techniques are needed. Methods: The study was a head-to-head comparison of laparotomy closure in an anatomically realistic surgical training mannequin (the “Cut-suit”) following a perihepatic DCS packing exercise. After skin-only closure with either the iTClamp or suture, the primary outcomes were completeness of closure and time to close. Six board-certified surgeons performed the same task as 12 military medical technicians (MT) who were randomized to unsupported (n = 5; UMT) or remotely telementored (n = 7; RTM) with real-time guidance by a trauma surgeon. Results: No study participants were able to close the incision with sutures and 0.55 achieved closure with the iTClamp. iTClamp application was superior to suturing when examining length of incision closed (p < 0.001), percent of incision closed (p < 0.001) and time to close incision (p = 0.008). Surgeons outperformed the RMT and UMTs on percent closed (p = 0.001, p = 0.004) and length closed (p = 0.001, p = 0.004) when suturing. However, MTs performed as well as the surgeons when using the iTClamp. All participants preferred the iTClamp to suturing, thought it was easy to use and learn to use and was applicable to DCS. Conclusion: Laparotomy closure following DCS in an anatomically realistic surgical mannequin was readily performed by military medical technicians and trained surgeons. The iTClamp proved to be a faster and easier modality to achieve incision closure compared to suturing.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".