Perspectives in adhesion prevention in gynaecological surgery
Bibliographic record
Abstract
Adhesions are a frequent, clinically relevant, and often costly complication of surgery that can develop in any body location regardless of the type of surgical procedure. Adhesions result from surgical trauma inducing inflammatory and coagulation processes and to date cannot be entirely prevented. However, the extent of adhesion formation can be reduced by using good surgical technique and the use of anti-inflammatory drugs, haemostats, and barrier agents. Strategies are needed in the short-, medium- and longer-term to improve the prevention of adhesions. In the short-term, efforts are needed to increase the awareness amongst surgeons and patients about the potential risks and burden of surgically induced adhesions. To aid this in the medium- term, a risk score to identify patients at high risk of adhesion formation is being developed and validated. Furthermore, available potentially preventive measures need to be highlighted. Both clinical and health economic evaluations need to be undertaken to support the broad adoption of such measures. In the longer- term, a greater understanding of the pathogenic processes leading to the formation of adhesions is needed to help identify effective, future treatments to reliably prevent adhesions from forming and lyse existing ones.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".