Predicting and Preventing Loss to Follow-up of Adult Trauma Patients in Randomized Controlled Trials
Notice bibliographique
Résumé
BACKGROUND: High loss-to-follow-up rates are a risk in even the most rigorously designed randomized controlled trials (RCTs). Consequently, predicting and preventing loss to follow-up are important methodological considerations. We hypothesized that certain baseline characteristics are associated with a greater likelihood of patients being lost to follow-up. Our primary objective was to determine which baseline characteristics are associated with loss to follow-up within 12 months after an open fracture in adult patients participating in the Fluid Lavage of Open Wounds (FLOW) trial. We also present strategies to reduce loss to follow-up in trauma trials. METHODS: Data for this study were derived from the FLOW trial, a funded trial in which payments to clinical sites were tied to participant retention. We conducted a binary logistic regression analysis with loss to follow-up as the dependent variable to determine participant characteristics associated with a higher risk of loss to follow-up. RESULTS: Complete data were available for 2,381 of 2,447 participants. One hundred and sixty-three participants (6.7%) were lost to follow-up. Participants who received treatment in the U.S. were more likely to be lost to follow-up than those who received treatment in other countries (odds ratio [OR] = 3.56, 95% confidence interval [CI]: 2.46 to 5.17, p < 0.001). Male sex (OR = 1.75, 95% CI: 1.15 to 2.67, p = 0.009), current smoking (OR = 1.82, 95% CI: 1.28 to 2.58, p = 0.001), high-risk alcohol consumption (OR = 1.88, 95% CI: 1.16 to 3.05, p = 0.010), and an age of <30 years (OR = 2.16, 95% CI: 1.19 to 3.95, p = 0.012) all significantly increased the odds of a patient being lost to follow-up. Conversely, participants who had sustained polytrauma (OR = 0.52, 95% CI: 0.37 to 0.73, p < 0.001) or had a Gustilo-Anderson type-IIIA, B, or C fracture (OR = 0.60, 95% CI: 0.38 to 0.94, p = 0.024) had lower odds of being lost to follow-up. CONCLUSIONS: Using a number of strategies, we were able to reduce the loss-to-follow-up rate to <7%. Males, current smokers, young participants, participants who consumed a high-risk amount of alcohol, and participants in the U.S. were more likely to be lost to follow-up even after these strategies had been employed; therefore, additional strategies should be developed to target these high-risk participants. CLINICAL RELEVANCE: This study highlights an important need to develop additional strategies to minimize loss to follow-up, including targeted participant-retention strategies. Male sex, an age of <30 years, current smoking, high-risk alcohol consumption, and treatment in a developed country with a predominantly privately funded health-care system increased the likelihood of participants being lost to follow-up. Therefore, strategies should be targeted to these participants. Use of the planning and prevention strategies outlined in the current study can minimize loss to follow-up in orthopaedic trials.
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 | 0,580 | 0,722 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,003 |
| Méta-épidémiologie (sens large) | 0,011 | 0,023 |
| Bibliométrie | 0,005 | 0,008 |
| Études des sciences et des technologies | 0,002 | 0,006 |
| Communication savante | 0,008 | 0,009 |
| Science ouverte | 0,005 | 0,004 |
| Intégrité de la recherche | 0,008 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
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; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».