Bibliographic record
Abstract
ECOG, CHAD, APACHE. These acronyms are part of the language of modern medicine, used daily by thousands of health professionals. Why are these clinical scores, used to categorise patients with cancer and cardiac and critical illnesses, so commonly used? In addition to their well established clinical validity, we believe the biggest reason is their simplicity: they are easy to remember and easy to use. In The Lancet Infectious Diseases, Matthew Saunders and colleagues1Saunders MJ Wingfield T Datta S et al.A household-level score to predict the risk of tuberculosis among contacts of patients with tuberculosis: a derivation and external validation prospective cohort study.Lancet Infect Dis. 2019; (published online Oct 30)https://doi.org/10.1016/S1473-3099(19)30423-2Summary Full Text Full Text PDF PubMed Scopus (26) Google Scholar derive and externally validate a household-level risk score to identify households of patients newly diagnosed with tuberculosis that are most likely to have another member develop active tuberculosis within 3 years. The complete risk score consists of 11 questions encompassing index patient, household, and contact characteristics, which should be answerable by the index patient. Using their risk score, the highest scoring third (618 of 1886) of households accounted for 72% (220 of 305) of all tuberculosis among contacts. When simplifying this risk score to only five questions, similar sensitivity was achieved among the highest scoring 37·5% (1540 of 4104) of households. Will this score be a winner? By this we mean, will this score become part of standard practice for tuberculosis programmes, at least in resource-limited settings, to help prioritise households for contact investigation? To be useful, a score should address an important and common problem—such as one that causes significant morbidity and mortality in a large number of people—be easy to use (and remember), and accurately identify the condition of interest—in this case high-risk households. WHO estimated that 10 million people developed tuberculosis in 2017.2WHOGlobal tuberculosis report: 2018. World Health Organization, Geneva2019Google Scholar Around 2 million of these people developed disease as a result of infection acquired recently from a household contact.3Churchyard G Kim P Shah NS et al.What we know about tuberculosis transmission: an overview.J Infect Dis. 2017; 216: S629-S635Crossref PubMed Scopus (107) Google Scholar Household tuberculosis contact investigations can identify members who already have active tuberculosis and a large number with latent infection, who are at high risk of developing disease, many within a short interval.4Fox GJ Nhung NV Sy DN et al.Household-contact investigation for detection of tuberculosis in Vietnam.N Engl J Med. 2018; 378: 221-229Crossref PubMed Scopus (112) Google Scholar For these reasons, household tuberculosis contact investigations have been a cornerstone of tuberculosis control programmes for decades. However, the implementation of these investigations in most high-incidence settings is impeded by a scarcity of resources and trained staff.5Hwang TJ Ottmani S Uplekar M A rapid assessment of prevailing policies on tuberculosis contact investigation.Int J Tuberc Lung Dis. 2011; 15: 1620-1623Crossref PubMed Scopus (31) Google Scholar, 6Ayakaka I Ackerman S Ggita JM et al.Identifying barriers to and facilitators of tuberculosis contact investigation in Kampala, Uganda: a behavioral approach.Implement Sci. 2017; 12: 33Crossref PubMed Scopus (53) Google Scholar, 7Tlale L Frasso R Kgosiesele O et al.Factors influencing health care workers' implementation of tuberculosis contact tracing in Kweneng, Botswana.Pan Afr Med J. 2016; 24: 229Crossref PubMed Scopus (6) Google Scholar A risk score could allow underfunded tuberculosis programmes to focus on households whose members are at highest risk of tuberculosis, maximising the public health benefit of preventive activities. Clearly this score meets our criteria of addressing a common and important public health problem. But is the score developed by Saunders and colleagues simple and easy to use? The ECOG and CHAD scores are based on two or three items that are readily gathered from the patient. Compared with these scores, the full 11-item questionnaire might be too lengthy for everyday use. The simplified risk score, with only five questions, of which four are routinely collected, seems much more likely to be useful for tuberculosis programmes and providers and to be acceptable to patients. Can the score accurately identify high-risk households? In the two study communities in Peru, the score performed well, but further validation outside of these communities is required. Notably, HIV serostatus of the index patient, or their household contacts, was not included in the risk score, owing to the very low prevalence (0·2%) of HIV in Peru.8UNAIDS UNAIDS, Peru2019http://www.unaids.org/en/regionscountries/countries/peruDate accessed: September 23, 2019Google Scholar We cannot assume the risk score will perform as well in settings with higher HIV prevalence, because HIV might affect the risk score in unexpected ways. However, in settings where many households affected by tuberculosis are also affected by HIV, the score could still be useful to better prioritise the highest-risk households and ensure that households that could benefit the most from contact investigations are reached. Another of Saunders and colleagues' findings was that a third of all tuberculosis occurred within the first 3 months of contact. However, a notable limitation of the risk score is that it does not predict when tuberculosis will develop. This limitation reinforces the need for timely contact investigations, consisting of thorough screening for both tuberculosis disease and infection, after an index patient has been diagnosed.9WHOLatent tuberculosis infection: updated and consolidated guidelines for programmatic management. World Health Organization, Geneva2018Google Scholar Household tuberculosis contacts are at high risk for tuberculosis and often further disadvantaged by sociocultural constructs that increase their susceptibility.10Hargreaves JR Boccia D Evans CA Adato M Petticrew M Porter JDH The social determinants of tuberculosis: from evidence to action.Am J Public Health. 2011; 101: 654-662Crossref PubMed Scopus (270) Google Scholar Developing and implementing tools to reach this population are crucial to tuberculosis prevention and elimination. The risk score developed by Saunders and colleagues is a potential tool that could allow already stretched tuberculosis programmes to focus their limited resources for prevention on the most vulnerable households. This online publication has been corrected. The corrected version first appeared at thelancet.com/infection oon November 6, 2019 This online publication has been corrected. The corrected version first appeared at thelancet.com/infection oon November 6, 2019 We declare no competing interests. We thank Mayara Bastos for reviewing this manuscript. A household-level score to predict the risk of tuberculosis among contacts of patients with tuberculosis: a derivation and external validation prospective cohort studyThis externally validated score will enable comprehensive biosocial, household-level interventions to be targeted to tuberculosis-affected households that are most likely to benefit. Full-Text PDF Open AccessCorrection to Lancet Infect Dis 2019; published online Oct 30. https://doi.org/10.1016/S1473-3099(19)30540-7Campbell JR, Menzies D. What makes a score a winner? Lancet Infect Dis 2019; published online Oct 30. https://doi.org/10.1016/S1473-3099(19)30540-7—In this Comment, Matthew Saunders' name has been corrected and the references renumbered. These corrections have been made as of Nov 6, 2019, and will be made to the printed version. Full-Text PDF Open Access
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".