Use of the iTClamp versus standard suturing techniques for securing chest tubes: A randomized controlled cadaver study
Bibliographic record
Abstract
OBJECTIVES: Tube thoracostomy (TT) is a common yet potentially life-saving trauma procedure. After successful placement however, securing a TT through suturing is a skillset that requires practice, risking that the TT may become dislodged during prehospital transport. The purpose of this study was to examine if the iTClamp was a simpler technique with equivalent effectiveness for securing TTs. MATERIALS AND METHODS: In a cadaver model, a 1.5 inch incision was utilized along the upper border of the rib below the 5th intercostal space at the anterior axillary line. TTs (sizes 28Fr, 32Fr, 36Fr and 40Fr) were inserted and secured with both suturing and iTClamp techniques according to the preset randomization. TT were then functionally tested for positive and negative pressure as well as the force required to remove the TT (pull test-up to 5 lbs). Time to secure the TT was also recorded. RESULTS: When sutured is placed by a trained surgeon, the sutures and iTClamp were functionally equivalent for holding a positive and negative pressure. Mean pull force for both sutures and iTClamp exceeded the 5 lb threshold; there was no significant difference between the groups. Securing the TT with the iTClamp was significantly faster (p < 0.0001) with the iTClamp having a mean application time of 37.0 ± 22.8 s and using a suture had a man application time of 96.3 ± 29.0 s. CONCLUSION: The iTClamp was effective in securing TTs. The main benefit to the iTClamp is that minimal skill is required to adequately secure a TT to ensure that it does not become dislodged during transport to a trauma center.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".