Wound swab quality grading is dependent on Gram smear screening approach
Bibliographic record
Abstract
Superficial skin swab collections are inherently low-quality and may be of little clinical value due to their poor sensitivity and specificity. Clinical microbiology laboratories can use Gram smears to screen and differentiate higher and lower quality specimens to direct the extent of potential pathogen work up, including antimicrobial susceptibility testing (AST). We compared the impact of two different smear grading approaches to our current reporting practices for superficial wound swab cultures. Two variations of the Q score methodology (low power under 10X (QS10) and high power under 100X (QS100) were compared to our existing oil immersion method (OM100) (100X). We further evaluated the QS100 method by scoring superficial swab smears previously screened by OM100 from cultures submitted between November 2018 and December 2019. No significant difference in the number of low-quality specimens (N = 50) was identified by QS10 or QS100 grading (N = 9; 18%; N = 8; 16% respectively). Among 968 additional QS100 screened smears, 67 (6.9%) low quality swabs were identified and 7.4% fewer organisms (76/1020 organisms) would require reporting with AST. Implementing the Q score for superficial wound swab cultures would provide minimal improvements in their clinical relevance, laboratory quality and efficiency in our laboratory due to the low number of poor-quality swabs received.
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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 | 0.016 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".