Quality of tuberculosis care in India: assessing diagnostic and treatment practices of health care providers
Bibliographic record
Abstract
Measure of Quality Measures Knowledge Measures Practice Accounts for Case-Mix* Accounts for Patient-Mix** Hawthorne Effects (i.e.behaviour is modified because of observer) Illnesses Covered and Other Remarks Vignettes Yes No Yes Yes Yes All Clinical Observation No Yes No No Yes Limited.First, "serious" illnesses like unstable angina will show up on a sporadic basis.Second, the observer never knows what the patient actually has, and doctors frequently make incorrect diagnoses.Chart Abstraction (health records) No Yes No No No Similar to clinical observation, but providers rarely keep patient charts.Also, charts tend to be incomplete and don't accurately reflect patient-provider interactions.Standardised Patients No Yes Yes Yes No Limited to (A) adults with non-critical illness only, (B) diseases that don't have any obvious findings on physical exam (which cannot be mimicked) and (C) conditions that don't require invasive exams.Initial costs are high.*Case mix indicates disease/illness spectrum and **patient mix indicates different sociodemographic characteristics.While questionnaires and vignettes provide insight into specific components of the knowledge of health care providers, these methods do not accurately reflect clinical practice.33,34 Direct clinical observation can provide information about practice, but this method also has its limitations.First, the presence of an observer may change the health care providers' behaviour 2.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".