AI dalam Kuis Pencocokan Uji Klinis

Jawaban dan penjelasan muncul saat Anda mengikuti kuis.

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. Mengapa membaca catatan klinis merupakan masalah yang sulit untuk perangkat lunak pencocokan?
   • Catatan selalu terstruktur dengan sempurna
   • Sebagian besar informasi penting terdapat dalam catatan teks bebas yang tidak terstruktur
   • Pasien tidak pernah membagikan catatan mereka
   • Semua rumah sakit menggunakan satu format yang identik
3. Apa yang dimaksud dengan 'kriteria pengecualian' kelayakan?
   • Menjamin pasien terdaftar
   • Menetapkan jumlah pendanaan uji coba
   • Daftar kondisi yang mendiskualifikasi pasien dari uji coba
   • Sebutkan nama peneliti utama uji coba tersebut
4. Kosakata standar apa yang mungkin digunakan AI untuk menormalkan konsep uji lab dari catatan?
   • 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