Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The first 6 months of the intern year was the most common period for NSIs, previously unreported in the literature.Dental residents were more likely to experience an NSI than other trainees, in contrast to literature findings that suggest surgery residents are at greatest risk. 8Previous literature excludes dental trainees.Dental residents may be more likely to experience an NSI based on the nature of their work (ie, the dark oral cavity with difficult illumination and learning mirrored image procedures).Resident education and training during orientation may reduce risk.For new residents, additional procedural skill simulation using sharp instruments may decrease NSI.However, a majority of residents felt comfortable in procedures with instruments causing injury. 3Despite resident-reported mastery, caution to avoid both overconfidence and decreased attention to NSI risk is warranted.We found that PGY-1 residents, especially during the first 6 months of training, are at greatest risk of NSI.Highest injury rates were observed for dentistry, obstetrics and gynecology, and surgery.Source patient seropositivity was low in this series.Simulation training during orientation and timeout reminders may increase procedural experience, decrease complacency, and reduce NSIs.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it