Factors associated with patient and health care system delay in the diagnosis of tuberculosis in France
Bibliographic record
Abstract
OBJECTIVE: To analyse diagnostic delay in tuberculosis (TB) patients. DESIGN: Cross-sectional study: all patients with TB notified to the French national surveillance system from April to June 2010 were interviewed face-to-face using a standardised questionnaire to assess symptom history and health-seeking trajectories. RESULTS: Of 225 patients enrolled, 172 (76.4%) had pulmonary TB, including 88 who were smear-positive. Mean delay between first symptoms and diagnosis (total delay) was 97 days (median 68, IQR 33-111), with a mean of 47 days (median 14, IQR 0-53) between first symptoms and health care contact (patient delay), and 48 days (median 25, IQR 6-67) between health care contact and diagnosis (health system delay). Factors independently associated with shortened total delay were medical insurance (OR 0.24, P = 0.014) and previous TB (OR 0.28, P = 0.049). Those associated with reduced patient delay were initial fever (OR 0.42, P = 0.03) and being followed by a general practitioner (OR 0.22, P = 0.004), while those associated with reduced health system delay were first health care contact within a hospital (OR 0.15, P < 0.001). Empirical antibiotic treatment was associated with increased health system delay (OR 4.4, P = 0.001). CONCLUSION: TB diagnostic delay needs to be reduced in France. This may be achieved through improved access to care, earlier hospital referral, and less use of empirical antibiotic treatment.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".