Effectiveness of a screening intervention to identify clinical-trial-eligible patients.
Notice bibliographique
Résumé
6069 Background: Advancements in cancer therapy require clinical trials, but only 3% of all cancer patients (CP) participate in trials causing many studies to be delayed or fail to complete. Literature indicates that accrual to clinical trials is primarily driven by MD related factors. The objective of this study was to evaluate whether the screening and identification of potentially eligible patients (PEP) for specific clinical trials would lead to an increased rate of accrual (RA). Methods: During a 4 month period, the charts of CP attending the outpatient clinics of 12 MDs were reviewed to determine eligibility for 21 phase II-IV trials for CP with BR, GI, GU, GY, or LG cancer. Trials were included if they had been open for ≥ 4 months and would remain open ≥ 8 months from the start of the intervention. A screening coordinator with minimal clinical background reviewed the electronic record of new and followup CP to determine eligibility according to protocol specified criteria. PEP were identified for medical oncologist by attaching notices to CP charts. Participating MDs were surveyed regarding the helpfulness and accuracy of the forms. A negative-binomial regression model was used to compare RA and find 95% CI for relative rates. Results: Between May 1 to August 31, 2011 a total of 2,098 charts were screened for eligibility for 21 trials, and 120 PEP were identified. Of these, 15 were randomized to the referred study, 4 to a different study, and 4 CP were offered but declined the referred study. Four month RA for included trials were 61 before, 73 during and 51 after the intervention. Relative rates adjusted for MD bookings were 0.85 (95% CI: 0.67, 1.06, p = 0.15) before and 0.70 (95% CI: 0.54, 0.90, p < 0.005) after, relative to during the intervention. 33 completed questionnaires were received: 22 (67%) were helpful and 23 (70%) were accurate. Screening required a 1.0 Full Time Equivalent position during the period of the intervention. Conclusions: Manual screening of patient records to determine clinical trial eligibility is labor intensive and increases enrolment to specific clinical trials. Notifications were deemed mostly helpful and accurate by oncologists. Screening interventions should be considered to improve clinical trial accrual.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,020 | 0,095 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,015 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».