Lessons on the quality of tuberculosis diagnosis from standardized patients in China, India, Kenya, and South Africa
Bibliographic record
Abstract
Standardized patients (SPs) are people who are recruited locally, trained to make identical scripted clinical presentations, deployed incognito to multiple different health care providers, and debriefed using a structured reporting instrument. The use of SPs has increased dramatically as a method for assessing quality of TB care since it was first validated and used for tuberculosis in 2015. This paper summarizes common findings using 3,086 SP-provider interactions involving tuberculosis across various sampling strata in published studies from India, China, South Africa and Kenya. It then discusses the lessons learned from implementing standardized patients in these diverse settings. First, quality is low: relatively few SPs presenting to a health care provider for the first time were given an appropriate diagnostic test, and most were given unnecessary or inappropriate medication. Second, care takes a wide variety of forms - SPs did not generally receive "wait and see" or "symptomatic" care from providers, but they received a medley of care patterns that included broad-spectrum antibiotics as well as contraindicated quinolone antibiotics and steroids. Third, there is a wide range of estimated quality in each observed sampling stratum: more-qualified providers and higher-level facilities performed better than others in all settings, but in every stratum there were both high- and low-quality providers. Evidence from SP studies paired with medical vignettes has shown that providers of all knowledge levels significantly underperform their demonstrated ability with real patients. Finally, providers showed little response to differences in patient identity, but showed strong responses to differences in case presentation that give some clues as to the reasons for these behaviors.
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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 | 0.059 | 0.165 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".