Prepared Endotracheal Tubes: Are They a Potential Source for Pathogenic Microorganisms?
Bibliographic record
Abstract
UNLABELLED: Prepared endotracheal tubes (PETTs) are frequently used for unanticipated difficult intubation, but their storage time is highly variable and institution-dependent. We sought to determine first, if open, unused PETTs are a potential source of pathogenic microorganisms, and second, if PETTs can provide a medium for bacterial survival after deliberate contamination. A stylet was inserted into a 7-mm ETT, and this system was ethylene oxide sterilized. The PETTs were placed in 20 different locations and sampled 8 times in a 4-wk period. Growth was determined after 48-h incubation, and the microorganism was identified. In Phase 2, the PETT (n = 40) was swabbed with a fresh suspension of H. influenzae, Pseudomonas aeruginosa, Staphylococcus aureus, Enterococcus faecium, or a negative control. Nonvirulent bacteria were cultured from 13 of 160 (8.1%) samples and from 15 of 320 (4.7%) samples in Phases 1 and 2, respectively. No PETT grew the same bacteria more than once. In Phase 2, after 24 h, only E. faecium was recovered. Based on this study, the pathogenic potential of PETTs is very small, and they can be safely used for up to 1 mo. This practice could translate to significant cost reduction for operating room budgets. IMPLICATIONS: Prepared endotracheal tubes (PETTs) are back-up airway equipment to be used in the case of a difficult intubation. A short PETT shelf life because of unknown safe storage time results in significant budget costs. This blinded, controlled study examined the pathogenic potential of PETTs in the operating room environment.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.004 | 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".