A cross-sectional validation study of using NERDS and STONEES to assess bacterial burden.
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
All chronic wounds are colonized by micro-organisms. Although the presence of bacteria is not necessarily harmful, and may be beneficial in some instances, accurate evaluation of wound-related bacterial damage and infection is crucial. A cross-sectional validation study involving 112 patients was conducted to estimate the specificity and sensitivity of clinical assessment variables individually and in combination to determine the presence and quantity of bacteria in the wound. The average age of study participants was 66 years (range 33 to 95 years) and most had leg (44) and foot (68) ulcers of approximately 6 months' duration. Wounds were evaluated using a mnemonic developed to evaluate the presence or absence of clinical signs of critical colonization (NERDS) or infection (STONEES) and results compared to semi-quantitative swab cultures. Wounds with debris, increased exudate, and friable tissue were found to be five times more likely to have scant or light bacterial growth; whereas, wounds with elevated temperature were eight times more likely to have moderate or heavy bacterial growth. When combining any three clinical signs, the sensitivity was 73.3% for scant or light and 90% for moderate and heavy bacterial growth and the specificity was 80.5% and 69.4%, respectively. Considering the importance of this clinical diagnosis, studies to examine the predictive validity of these assessment variables and culture results are warranted.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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