Next generation sequencing of bacteria to control Ciprofloxacin and amoxyclav antibiotic resistance in ear infections
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
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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