A Vignette‐Based Survey to Assess Clinical Decision Making Regarding Antibiotic Use and Hospitalization of Patients with Probable Aseptic Meningitis
Bibliographic record
Abstract
BACKGROUND: The many etiologies of meningitis influence disease severity - most viral causes are self-limiting, while bacterial etiologies require antibiotics and hospitalization. Aided by laboratory findings, the physician judges whether to admit and empirically treat the patient (presuming a bacterial cause), or to treat supportively as if it were viral. OBJECTIVE: To determine factors that lead infectious disease specialists to admit and treat in cases of suspected meningitis. METHODS: A clinical vignette describing a typical case of viral meningitis in the emergency department was presented to clinicians. They were asked to indicate on a Likert scale the likelihood of administering empirical antibiotics and admitting the patient from the vignette and for eight subsequent scenarios (with varied case features). The process was repeated in the context of an inpatient following initial observation and/or treatment. RESULTS: Participants were unlikely to admit or to administer antibiotics in the baseline scenario, but a low Glasgow Coma Score or a high cerebrospinal fluid (CSF) white blood cell count with a high neutrophil percentage led to empirical treatment and admission. These factors were less influential after a negative bacterial CSF culture. These same clinical variables led to maintaining treatment and hospitalization of the inpatient. CONCLUSIONS: Most participants chose not to admit or treat the patient in the baseline vignette. Confusion and CSF white blood cell count (and neutrophil predominance) were the main influences in determining treatment and hospitalization. A large range of response scores was likely due to differing regional practices or to different levels of experience.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".