Gradient diffusion susceptibility testing for Neisseria gonorrhoeae: an accurate alternative to agar dilution in high-MIC strains?
Bibliographic record
Abstract
INTRODUCTION: is not well established, especially in strains with high MICs. AIM: . METHODS: , all tested by the agar dilution method according to CLSI methods and confirmed to be genetically distinct using molecular typing (NG-MAST), were selected. Isolates with high MICs were targeted. Gradient diffusion was performed for ceftriaxone (CRO), cefixime (CFX), azithromycin (AZT), tetracycline (TET) and fosfomycin (FOS) using two different commercial antimicrobial strips on different culture media (a non-commercial GC agar base with 1 % defined growth supplement and two commercial media). The performance of agar gradient diffusion was assessed based on accuracy, using essential and category agreements (EA and CA). RESULTS: Essential and categorical agreement were over 90 % for CRO, CFX and AZT on the two commercial agar media tested. Category disagreements were seen for CFX and AZT, mostly just very major errors. For TET, EA ranged from 80 to 96 % and CA ranged from 38 to 76 %, most of the misclassifications being minor errors. Finally, EA for FOS ranged between 80 and 98 %. CONCLUSION: Gradient diffusion is an accurate and acceptable alternative for CRO, CFX and AZT. Caution is advised when MICs are reported by gradient diffusion approach breakpoints because of the possibility of very major errors. The use of gradient diffusion is limited for TET because of the high rate of minor errors.
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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".