Delays in hospital admissions for patients with fractures – Authors' reply
Notice bibliographique
Résumé
We thank Jayadevan Sreedharan and colleagues for their valuable perspective on our Article.1Pouramin P Li CS Busse JW et al.Delays in hospital admissions in patients with fractures across 18 low-income and middle-income countries (INORMUS): a prospective observational study.Lancet Glob Health. 2020; 8: e711-e720Summary Full Text Full Text PDF PubMed Scopus (12) Google Scholar INORMUS is a multicountry study that aims to assess fracture care in low-income and middle-income countries. Given constraints in low-resource settings, eight participating hospitals did not have the capacity to enrol patients every day. Thus, to ensure the fidelity of the data collection process, a schedule was implemented to enrol all eligible patients on consistent days in the week (≥3 days per week). This schedule ensured that patients were both cared for and enrolled as consistently as possible. Although, as Sreedharan and colleagues have suggested, sampling is not necessary when analysing all fracture patients, we incorporated this approach to ensure high quality data. Large sample sizes increase the likelihood of statistical significance2Kaplan RM Chambers DA Glasgow RE Big data and large sample size: a cautionary note on the potential for bias.Clin Transl Sci. 2014; 7: 342-346Crossref PubMed Scopus (154) Google Scholar and might increase the apparent importance of small effects. The magnitude of risk of increasing age on 2-h admission delay in patients with open fractures (risk ratio [RR] 1·005, 99% CI 1·001–1·009) and 24-h admission delay in patients with closed fractures (1·008, 99% CI 1·005–1·010) were rounded up due to journal formatting requirements. Thus, the risk of admission delay increases by 0·5% per 1-year increment of age in patients with open fractures and 0·8% per 1-year increment of age in patients with closed fractures. These risks become evident among older populations, where, for example, a patient aged 70 years with a closed fracture would be at a 40% increased risk for delay compared with a similar patient aged 20 years, which we thought to be clinically important. To strengthen this interpretation, our results are consistent with observations of Nkurunziza and colleagues3Nkurunziza T Toma G Odhiambo J et al.Referral patterns and predictors of referral delays for patients with traumatic injuries in rural Rwanda.Surgery. 2016; 160: 1636-1644Summary Full Text Full Text PDF PubMed Scopus (20) Google Scholar who reported an association between older age and interfacility referral delays among injured patients in Rwanda (odds ratio for age >35 years: 2·45, 95% CI 1·09–5·50). However, we acknowledge that by reporting the association between age and delay in admission to hospital, age could have been categorised by decade or by age groups, instead of by year. We believe our exploratory analysis has highlighted many important risk factors, including the risk of interfacility transfers, and spine and pelvic fractures, among others, that are supported by observations in the field.3Nkurunziza T Toma G Odhiambo J et al.Referral patterns and predictors of referral delays for patients with traumatic injuries in rural Rwanda.Surgery. 2016; 160: 1636-1644Summary Full Text Full Text PDF PubMed Scopus (20) Google Scholar, 4Kuzma K Lim AG Kepha B Nalitolela NE Reynolds TA The Tanzanian trauma patients' prehospital experience: a qualitative interview-based study.BMJ Open. 2015; 5e006921Crossref PubMed Scopus (21) Google Scholar However, future work will be needed to delineate more detailed mechanisms of delays in admission to hospital. We remain confident in our main conclusion that, globally, access to timely admission remains largely inaccessible among low-income and middle-income countries, and must be addressed. MB reports receiving consulting fees from AgNovos Healthcare, and Pendopharm, and receives grant support from DJ Orthopedics and Acumed. MB reports receiving grant support from the Medical Research Council of Australia, Canadian Institutes of Health Research, McMaster Surgical Associates, and Hamilton Health Sciences. All other authors declare no competing interests. Delays in hospital admissions in patients with fractures across 18 low-income and middle-income countries (INORMUS): a prospective observational studyIn low-income and middle-income countries, timely hospital admission remains largely inaccessible, especially among patients with open fractures. Reducing hospital-based delays in receiving care, and, in particular, improving interfacility referral systems are the most substantial tools for reducing delays in admissions to hospital. Full-Text PDF Open AccessDelays in hospital admissions for patients with fracturesWe read with interest the Article by Panthea Pouramin and colleagues in The Lancet Global Health.1 The study prospectively observes the percentage of delays in hospital admission 18 low-income and middle-income countries as a surrogate for accessing timely fracture care among the patient group with open fractures and closed fractures, based on a delay of more than 2 h for open fractures and more than 24 h for closed fractures. Additionally, exploring the factors associated with delayed admission to hospital, the authors concluded that timely hospital admission for these patients is largely inaccessible in low-income and middle-income countries. Full-Text PDF Open Access
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 | 0,009 | 0,142 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,003 | 0,008 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,020 | 0,025 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,006 |
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 ».