Bibliographic record
Abstract
The purpose of the present work is to provide a fresh, simple, and accessible document for all surgeons who treat traumatic hemorrhage from the head and neck. This article arose from the work of a consortium of experienced trauma surgeons who collaborated to produce a first-of-its-kind surgical course for multifocal hemorrhage control. The "Bloody Simple Hemorrhage control masterclass course" has been offered at national and international venues since 2019 and has been both well received by participants and well regarded in academic trauma surgical circles. This paper—and the series of articles which accompany it—was meant to be a literature companion to or extension of the Bloody Simple course, a way to distill and digest the hemorrhage control strategies espoused therein but in the form of a journal article. The result of this work is a succinct and experience-based set of principles for conquering life-threatening, traumatic bleeding from a variety of sources in the head and neck. This article translates experience and evidence into a simple and digestible format that will provide a sound approach for any surgeon facing traumatic hemorrhage from the head and neck.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".