Internal Medicine Resident Computer Usage
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.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.075 | 0.013 |
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".