The predicament of patients with suspected Ebola
Bibliographic record
Abstract
In their Comment in The Lancet Global Health, Eugene Richardson and colleagues1Richardson ET Barrie MB Nutt CT et al.The Ebola suspect's dilemma.Lancet Glob Health. 2017; 5: e254-e256Summary Full Text Full Text PDF PubMed Scopus (25) Google Scholar criticised the tendency of many analyses of the Ebola epidemic (eg, a WHO report2Agua-Agum J Allegranzi B Ariyarajah A et al.WHO Ebola Response TeamAfter Ebola in West Africa—unpredictable risks, preventable epidemics.N Engl J Med. 2016; 375: 587-596Crossref PubMed Scopus (181) Google Scholar) to ignore that it may be rational for a patient with a fever to avoid an Ebola treatment unit. They use the prisoner's dilemma to explain such non-cooperative behaviour. The prisoner's dilemma, however, is not the most appropriate analytical framework for this situation. It involves two parties, each with their own interests, while the patient's dilemma might better be understood as a game against nature, ie, without a rational and self-interested opponent. We suggest that the threshold approach introduced by Pauker and Kassirer3Pauker SG Kassirer JP Therapeutic decision making: a cost-benefit analysis.N Engl J Med. 1975; 293: 229-234Crossref PubMed Scopus (339) Google Scholar, 4Djulbegovic B Van den Ende J Hamm RM Mayrhofer T Hozo I Pauker SG International Threshold Working GroupWhen is rational to order a diagnostic test, or prescribe treatment: the threshold model as an explanation of practice variation.Eur J Clin Invest. 2015; 45: 485-493Crossref PubMed Scopus (31) Google Scholar better explains the described phenomenon. The threshold model prescribes a probability of disease at which treatment becomes a better option than no treatment. The threshold is a function of the relative effects of the possible actions and compares the benefit of treating a true Ebola patient against the harm of treating a non-Ebola patient. In this example, exposure to the virus from contact with other (true) Ebola patients represents the harm condition. Using the mortality numbers provided,1Richardson ET Barrie MB Nutt CT et al.The Ebola suspect's dilemma.Lancet Glob Health. 2017; 5: e254-e256Summary Full Text Full Text PDF PubMed Scopus (25) Google Scholar the benefit is the mortality reduction for true Ebola patients (70·8%–64·3%=6·5%), while the harm is the mortality increase for patients without Ebola (16·1%–0·2%=15·9%). The treatment threshold is calculated as harm/(harm+benefit). Given these data, the treatment threshold is 71·0% (figure). If individuals with suspected Ebola assume that their probability of having Ebola is below this threshold—eg, Richardson and colleagues1Richardson ET Barrie MB Nutt CT et al.The Ebola suspect's dilemma.Lancet Glob Health. 2017; 5: e254-e256Summary Full Text Full Text PDF PubMed Scopus (25) Google Scholar assume a probability of 50%—the rational behaviour from the individual's point of view is to not seek treatment. In conclusion, the threshold model3Pauker SG Kassirer JP Therapeutic decision making: a cost-benefit analysis.N Engl J Med. 1975; 293: 229-234Crossref PubMed Scopus (339) Google Scholar, 4Djulbegovic B Van den Ende J Hamm RM Mayrhofer T Hozo I Pauker SG International Threshold Working GroupWhen is rational to order a diagnostic test, or prescribe treatment: the threshold model as an explanation of practice variation.Eur J Clin Invest. 2015; 45: 485-493Crossref PubMed Scopus (31) Google Scholar might explain patients' avoidance of Ebola treatment better and more elegantly than the prisoner's dilemma does. We declare no competing interests. The Ebola suspect's dilemmaIn 1950, Merrill Flood and Melvin Dresher of the RAND Corporation developed a theoretical model of cooperation and conflict, which was later formalised by Albert W Tucker as the prisoner's dilemma.1 This model represents a situation in which two prisoners each have the option to confess or not, but their sentencing outcomes depend crucially on the simultaneous choice of the other (figure).1 Fittingly, it has become the paradigmatic example of individual versus group rationality and is an often used heuristic when conveying introductory social theory to students. 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 | 0.011 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.021 | 0.028 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".