Oral fluoroquinolones and risk of secondary pseudotumor cerebri syndrome
Bibliographic record
Abstract
OBJECTIVE: To quantify the risk of secondary pseudotumor cerebri syndrome (PTCS) with fluoroquinolones. METHODS: A case-control study of people 15-60 years of age from the LifeLink Database (QuintilesIMS, Parsippany, NJ) was conducted. Cases had the first ICD-9-CM code for benign intracranial hypertension (BIH) as well as having received a procedure code for an MRI or CT scan and a lumbar puncture within 15 days or 30 days of the BIH code. For each case, 10 controls were selected using density-based sampling. Current users of fluoroquinolones received a prescription within 15 days or 30 days of the date of the diagnosis. For the sensitivity analysis, risk periods for 30 and 60 days were also examined. Adjusted rate ratios (RRs) were computed from a conditional logistic regression model. RESULTS: From a cohort of 6,110,723 people, there were 339 cases of PTCS and 3,390 corresponding controls. In the primary analysis, the adjusted RR for current users of fluoroquinolones for both the 15-day and 30-day definitions were 5.67 (95% confidence interval [CI] 2.72-11.83) and 4.15 (95% CI 2.29-7.50), respectively. The risk with tetracycline antibiotics was also increased, with RRs for 15 and 30 days of current use of 2.68 (0.89-8.11) and 3.64 (1.67-7.91), respectively. CONCLUSION: Our study suggests an increase in the risk of PTCS with current users of fluoroquinolones. Although this adverse event is rare, patients who experience symptoms of raised intracranial pressure including headaches, tinnitus, and double vision while taking fluoroquinolones should seek medical attention.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".