Therapeutic Application of Fibrinogen in Spine Surgery: A Review Article
Bibliographic record
Abstract
BACKGROUND: The aim of this review is to investigate current uses of fibrinogen as a tool to reduce operative and postoperative blood loss in different surgical fields especially orthopedic spine surgery. This is a systematic review. METHODS: MEDLINE (via Ovid 1946 to June 1, 2020) and Embase (via Ovid 1947 to June 1, 2020) were searched using the keywords "fibrinogen", "surgery", and "spine" for relevant studies. The search strategy used text words and relevant indexing to identify articles discussing the use of fibrinogen to control surgical blood loss. RESULTS: The original literature search yielded 407 articles from which 68 duplications were removed. Three hundred thirty-nine abstracts and titles were screened. Results were separated by surgical specialties. CONCLUSIONS: Multiple studies have looked at the role of fibrinogen for acute bleeding in the operative setting. The current evidence regarding the use of fibrinogen concentrate in spine surgery is promising but limited, even though this is a field with the potential for severe hemorrhage. Further trials are required to understand the utility of fibrinogen concentrate as a first-line therapy in spine surgery and to understand the importance of target fibrinogen levels and subsequent dosing and administration to allow recommendations to be made in this field.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".