Next generation sequencing of bacteria to control Ciprofloxacin and amoxyclav antibiotic resistance in ear infections
Bibliographic record
Abstract
Long standing ear infection or chronic suppurative otitis media (CSOM) is the inflammation of middle ear cleft persistent or intermittent infected ear discharge from a non-intact perforated tympanic membrane at least for 3 months of duration. Commonly ciprofloxacin and amoxyclav are used as safe and popular antibiotics for CSOM. But unfortunately the antibiotics become resistant due to the over use or miss use. To know the culture sensitivity of antibiotic pattern, it requires minimum 5 days and after getting the result, the clinician may or may not prescribe these antibiotics. But it is a time consuming method. The recent improvements in sequencing technologies, next generation sequencing (NGS) are positioned to become an essential tool in the control of antibiotic resistance, a major threat in modern healthcare. NGS has already found numerous applications in this area, ranging from the development of novel antibiotics and diagnostic tests through to antibiotic stewardship of currently available drugs via surveillance and the elucidation of the factors that allow the emergence and persistence of resistance. Numerous techniques can be developed in the value of NGS as a tool for infection control caused by bacteria as a primary diagnostic tool to detect ciprofloxacin and amoxyclav antibiotic resistance. However, appropriate data analysis platforms will need to be developed before routine NGS can be introduced on a large scale. The result will reveal the early detection of the efficacy of these antibiotics (ciprofloxacin and amoxyclav) with the clinic-microbiological profile of CSOM and to analyze the susceptibility pattern of the aerobic bacterial isolates, so that an antibiotic policy can be formulated for CSOM, for better patient management.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".