Comments to regional problems of analgesic risk perception.
Bibliographic record
Abstract
OBJECTIVES: The aim of our study was to analyse analgesic risk perception and then to compare analgesic drug choice among general practitioners. METHOD: The structured questionnaire was used and completed during continuous medical education lectures. Series of targeted open or close questions and visual analog scale (VAS) to determine drug risk perception were used. Slovak general practitioners attending continuous medical education lectures during 2004-2005 were invited to participate in the study. Group 1 consisted of respodents from Bratislava (capital city of Slovakia, n = 245) and group 2 consisted of general practitioners from 3 other cities (middle and eastern Slovakia, n = 325). Data were compared to reported adverse drug reactions. RESULTS: Quarter of doctors 25.3% (n = 62), (25.2% (n = 82) respectively), considered non-steroidal anti-inflammatory drugs to be the safest group of analgesics. Gastrointestinal damage in general was perceived as most common adverse drug reaction. 72.41% (75.94% respectively) of respondents considered analgesics as exactly or probably danger. Perceived drug risk labeled on VAS was 4.23 (SD 1.52), (3.22 (SD 2.19) respectively) (p < 0.05). Total number of reported adverse drug reactions in years 1998-2002 was 3249, 412 were related to analgesic use. Specific organotoxic adverse drug reactions (nephrotoxicity, etc.) were reported rarely. CONCLUSION: The actual perception of analgesic risk in Slovakia seems to be generally inadequate. We found only a low support of spontaneous adverse drug reactions reporting to the national monitoring system (Tab. 1, Fig. 2, Ref. 11).
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".