A Cross-Sectional Study of Bacteriological Profiles in Patients with Chronic Suppurative Otitis Media at a Medical College
Bibliographic record
Abstract
Background and Aim: Persistent suppurative otitis media is characterized by persistent inflammation of the middle ear and mastoid cavity, which can manifest as recurrent ear discharges or otorrhoea through a tympanic hole. The purpose of this study is to evaluate the microbiological profile of ear infections in hospitalised patients. Material and Methods: The current cross-sectional study was carried out in the Department of Otorhinolaryngology, Medical College and Hospital, in collaboration with the Department of Microbiology. A total of 200 patients attending the ENT department of Medical College and Hospital, who satisfied the inclusion criteria were included in the study. complete clinical ENT examination carried out, aural swabs were collected, culture was done and antibiotic sensitivity was studied. Results: A total of 75% of the isolates exhibited pure growth, 20% showed mixed growth, and 5% showed no development. As pure growth, Staphylococcus aureus (70/150) and Pseudomonas aeruginosa (48) were identified as the most prevalent causal microorganisms. The remaining isolates grew various bacteria, including Klebsiella spp. (8.0%) and Escherichia coli spp. (5.3%). Proteus spp. (5.3%), Enterobacter spp. (1.3%), and Citrobacter sp. (1.3%) were the most common. As mixed growth, S. aureus (50.0%) and P. aeruginosa (30.0%) were identified as the most prevalent causal microorganisms. Conclusion: Ear infection is a serious public health issue in poor nations such as India. Early detection of etiological agents and knowledge of their antibiotic sensitivity pattern can help reduce the prevalence of ear infections.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".