Multilaboratory Study of the Biomic Automated Well-Reading Instrument versus MicroScan WalkAway for Reading MicroScan Antimicrobial Susceptibility and Identification Panels
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
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.
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 | 0.002 | 0.002 |
| 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 | 0.000 | 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