Exploring the need for surgical face masks in operating room: a comprehensive literature review
Bibliographic record
Abstract
Surgical face masks (SFM) are pivotal in preventing surgical site infections (SSI) in the operating room (OR). However, there are currently no specific recommendations for their most effective use. SFM effectiveness is influenced by factors such as material, fit, and duration of use, sparking ongoing debates about their benefits and risks in surgery. SFMs act as a protective barrier, but their ability to filter out harmful compounds is questioned. They can also impact communication and create a false sense of security. Nevertheless, SFMs aid in infection prevention and provide psychological comfort. Clear guidelines are needed to ensure their appropriate use in the OR. This paper offers a historical overview of surgical masks, emphasizing their role in infection prevention. It explores SFM effectiveness for both the surgical team and patients during surgery and considers their future in surgical settings. As we navigate the evolving landscape of SFMs, clear and concise guidelines are imperative to ensure their judicious and effective use in the OR. This paper serves as an essential resource for understanding the historical significance, contemporary efficacy, and prospective trajectory of SFMs in surgical practice.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".