Corneal infection in Shandong peninsula of China: a 10-year retrospective study on 578 cases
Bibliographic record
Abstract
AIM: To determine the epidemiological characteristics, clinical signs, laboratory findings, and outcomes in patients with corneal infection in Shandong peninsula of China. METHODS: The medical records of 578 inpatients (578 eyes) with corneal infection were reviewed retrospectively for demographic characteristics, risk factors, seasonal variation, clinical signs, laboratory findings, and treatment strategy. Patient history, ocular examination findings using slit-lamp biomicroscopy, laboratory findings resulted from microbiological cultures, and treatment. RESULTS: Fungal keratitis constituted 58.48% of cases of infectious keratitis among the inpatients, followed by herpes simplex keratitis (20.76%), bacterial keratitis (19.03%) and acanthamoeba keratitis (1.73%). The most common risk factor was corneal trauma (71.80%). The direct microscopic examination (338 cases) using potassium hydroxide (KOH) wet mounts was positive in 296 cases (87.57%). Among the 298 fungal culture-positive cases, Fusarium species were the most common isolates (70.47%). A total of 517 cases (89.45%) received surgical intervention, including 255 (44.12%) cases of penetrating keratoplasty, 74 (12.80%) cases of lamellar keratoplasty which has become increasingly popular, and 77 cases (13.32%) of evisceration or enucleation. CONCLUSION: At present, infectious keratitis is a primary corneal disease causing blindness in China. With Fusarium species being the most commonly identified pathogens, fungal keratitis is the leading cause of severe infectious corneal ulcers in Shandong peninsula of China.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".