Prueba de emparejamiento de IA en ensayos clínicos

Las respuestas y explicaciones aparecen a medida que realiza el cuestionario.

1. What task can clinical trial matching software support?
   • Compare documented patient information with trial eligibility criteria
   • Guarantee enrollment by a deadline
   • Replace all clinical judgment
   • Prove a treatment is effective before a trial
2. ¿Por qué la lectura de registros clínicos es un problema difícil para el software de comparación?
   • Los registros siempre están perfectamente estructurados.
   • Gran parte de la información clave se encuentra en notas de texto libre no estructuradas.
   • Los pacientes nunca comparten sus registros
   • Todos los hospitales utilizan un formato idéntico
3. ¿Qué hace un 'criterio de exclusión' de elegibilidad?
   • Garantiza que un paciente esté inscrito
   • Establece el monto de financiación de la prueba.
   • Enumera las condiciones que descalifican a un paciente de un ensayo.
   • Nombra al investigador principal del ensayo
4. ¿Qué vocabulario estandarizado podría usar una IA para normalizar los conceptos de las pruebas de laboratorio a partir de los registros?
   • HTML
   • JPEG
   • HTTP
   • LOINC
5. Why might a team evaluate trial-matching results across patient subgroups?
   • To check whether candidate rankings or eligibility extraction differ across groups
   • To guarantee that every subgroup has equal trial access
   • To remove eligibility criteria from the protocol
   • To prove a trial has no recruitment bias
6. Which is a reasonable human-review option in a trial-matching workflow?
   • Automatically enroll every high-scoring candidate
   • Allow a qualified clinician or study team to review candidate matches before action
   • Let a similarity score override protocol criteria
   • Treat a model match as confirmation of eligibility
7. When an eligibility criterion is vague, what can embedding similarity contribute to a patient-trial match?
   • A final enrollment decision without a clinician
   • A new laboratory result not present in the chart
   • A semantic similarity signal to help rank possible matches for clinician review
   • A guarantee that the patient meets every inclusion criterion
8. In the guide's matching pipeline, what is a rule engine used for after patient and trial information are normalized?
   • Generating diagnoses that are absent from the chart
   • Replacing clinical review for every uncertain match
   • Ranking hospitals by their billing totals
   • Checking hard eligibility constraints such as age or lab thresholds