The iTClamp in the treatment of prehospital craniomaxillofacial injury: a case series study
Bibliographic record
Abstract
BACKGROUND: Craniomaxillofacial (CMF) injuries are very common in both civilian and military settings. Nearly half of all civilian trauma incidents include a scalp laceration and historical rates of CMF battle injuries increased from 16%-21% to 42.2%. The scalp is highly vascular tissue and uncontrolled bleeding can lead to hypotension, shock and death. Therefore, enabling on-scene providers, both military and civilian, to immediately manage scalp and face lacerations, in a manner that allows them to still function in a tactical way, offers operational advantages. This case series examines how effectively a wound-clamp (iTClamp) controlled bleeding from CMF injuries pre-hospital environment. METHODS: The use of the iTClamp for CMF (scalp and face laceration) was extracted from iTrauma Care's post market surveillance database. Data was reviewed and a descriptive analysis was applied. RESULTS: 216 civilian cases of iTClamp use were reported to iTrauma Care. Of the 216 cases, 37% (n=80) were for control of CMF hemorrhage (94% scalp and 6% face). Falls (n=24) and MVC (n=25) accounted for 61% of the mechanism of injury. Blunt accounted for 66% (n=53), penetrating 16% (n=13) and unknown 18% (n=14). Adequate hemorrhage control was reported in 87.5% (n=70) of cases, three respondents reported inadequate hemorrhage control and in seven cases hemorrhage control was not reported. Direct pressure and packing was abandoned in favor of the iTClamp in 27.5% (n=22) of cases. CONCLUSIONS: CMF injuries are common in both civilian and military settings. Current options like direct manual pressure (DMP) often do not work well, are formidable to maintain on long transports and Raney clips are a historical suggestion. The iTClamp offers a new option for control of external hemorrhage from open wounds within compressible zones.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".