Delays in hospital admissions for patients with fractures – Authors' reply
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.142 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.020 | 0.025 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".