Appropriateness of Triple Therapy after COPD Exacerbation
Bibliographic record
Abstract
number of units (e.g.tablets, vials) of drug samples in all outpatient clinics was counted.The audit was repeated in November 2009 and July 2012.For each audit period, the total numbers of both units and doses of drug samples were calculated, and the average number of doses was estimated for liquids (0.5 mL/dose) and topical agents (0.5 g/dose).The number of doses of drug samples per patient visit was also calculated, to indicate potential exposure of patients to samples.In total, 31 locations (i.e., health care units) were identified in 21 outpatient clinics.A total of 14 221 units of drug samples were counted in 2007, 8080 units in 2009, and 6989 units in 2012 (see details in Table 1).Although the number of units decreased over time, the number of doses increased, from 78 955 in 2007 to 75 487 in 2009 and 91 000 in 2012 (breakdown by clinic not shown), mostly because of a higher proportion of topical drugs in the dermatology clinic.The number of doses of drug samples per patient visit remained stable: 0. 40 in 2007, 0.38 in 2009, and 0.41 in 2012.In 2012, only 19% of doses documented during the audit were listed on the official hospital drug formulary; in addition, 4% of the doses were expired.Despite implementation of a Web-based intranet form to declare drug samples received from industry sales representatives, most doses of drug samples had not been declared to the pharmacy by hospital staff.The availability of drug samples in outpatient clinics at the study hospital has remained stable for the past 5 years.It may seem feasible to prohibit the distribution of samples locally in outpatient clinics, but in fact, it is difficult to do so when such distribution is not prohibited by the pertinent regulatory authorities.For instance, physicians and medical residents often work in multiple hospitals, and their regulations regarding drug samples may vary.We believe that drug samples do not contribute to better patient care and should only be dispensed by retail pharmacies through a structured approach, with documentation of doses dispensed in the patient's record.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".