Establishing a role for intra-pleural fibrinolysis in managing traumatic haemothoraces
Bibliographic record
Abstract
A best evidence topic in thoracic surgery was written according to a structured protocol. The question addressed was whether there is a role in using intra-pleural fibrinolysis or thrombolysis with an agent such as streptokinase aids in resolving haemothoraces following trauma. Twenty-four papers were identified using the search below. Eight papers presented the best evidence to answer the clinical question. The author, journal, date and country of publication, patient group studied, study type, relevant outcomes, results, and study weaknesses of the papers are tabulated. We conclude that intra-pleural fibrinolytic does have a role in managing patients with unresolved haemathoraces with complete resolution clinically and radiologically in most patients in half of the studies reviewed. It may be used as an alternative to surgical intervention in certain patients, but little work has been done on comparing intra-pleural fibrinolysis directly to surgical evacuation. The choice of agent and number of administrations are variable but with a similar outcome. Few studies have compared agents. The timing of when to use these agents following the traumatic haemothorax was variable but its use was commonly reserved following 'failure' of chest drainage clinically or radiologically (so usually over a week following the original injury). The overall morbidity including bleeding complications from their use was reported as low.
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 | 0.037 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.013 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".