Multilaboratory Study of the Biomic Automated Well-Reading Instrument versus MicroScan WalkAway for Reading MicroScan Antimicrobial Susceptibility and Identification Panels
Bibliographic record
Abstract
This study compared the Biomic automated well reader results to the MicroScan WalkAway results for reading MicroScan antimicrobial susceptibility and identification panels at four different sites. Routine fresh clinical isolates and quality control (QC) organisms were tested at each study site. A total of 46,176 MicroScan panel drug-organism combinations were read. The Biomic category agreement for 3,117 Gram-negative bacteria was 98.4%, with 1.4% minor and 0.2% major discrepancies. The Biomic category agreement for 5,233 Gram-positive bacteria was 98.7%, with 0.9% minor, 0.3% major, and 0.1% very major errors. Essential agreement, defined as Biomic results that were within ±1 2-fold dilution of the MicroScan results, was 99.3% for Gram-negative bacteria and 98.3% for Gram-positive bacteria. Biomic reading of MicroScan identification panels provided an overall agreement (first- and second-choice organism match) of 99.5% with 846 Gram-negative isolates and 99.5% with 430 Gram-positive isolates. These results suggest that the Biomic automated reader can provide accurate reading of MicroScan panels and has the capability of a visual panel read for manual adjustment of results.
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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 | 0.086 | 0.212 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".