Comparative effectiveness of an interactive survey and the traditional method for collecting active symptoms and medical history
Bibliographic record
Abstract
The purpose of the study was to compare the results of an interactive survey and the traditional method for collecting active symptoms and medical history in patients with respiratory diseases. Methods. The study included 82 patients with respiratory conditions: 45 patients with broncho-obstructive diseases and 37 patients with community-acquired pneumonia, who were divided into subgroups depending on their scores on the Morisky-Green rating scale and the Toronto Аlexithymia Scale. The interactive survey was conducted using the respiratory module of the automated system for preliminary syndromic diagnosis “Electronic Clinic”. Statistical analysis of the study results was carried out using the Statistica 10.0 program. Results. The interactive survey identified the following symptoms of diseases more often than the traditional collection of active symptoms and medical history: productive cough, feeling of chest congestion, wheezing (p < 0.05), and shortness of breath (p > 0.05). Conclusion. An interactive survey allows identifying more details about the patient-reported symptoms and minimize the risk of missing important medical information when the doctor is pressed for time or the patients are non-cooperative or have difficulty communicating their feelings and sensations.
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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 | 0.015 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".