[Assessment of tigecycline use economic impact in first-line therapy for complicated intra-abdominal infections in an Intensive Care Unit].
Bibliographic record
Abstract
The aim of this study was to determine the economic impact on hospital budget expenditure for two different prescribing practices: use of tigecycline in second or first-line therapy (when appropriate). This empirical study was carried out at the Intensive Care Unit (ICU) (Chief, Dr. Alberto Costantini), Ospedali Riuniti, Ancona. Cost determination was based on health care processes as revealed by field survey at the ICU. Mapping of the health care processes was neither derived from official protocols nor from an ex-post analysis of medical records but rather directly from descriptions of the processes as referred by the ICU physicians and health care staff, and then summarized in flow charts and approved by the ICU chief. The assumption was that tigecycline, because of its broader spectrum of action, would more probably clear infections when used in the first course of antibiotic therapy. Notwithstanding this advantage, tigecycline has a higher daily dose cost than first-line antibiotics. This study compared the higher costs incurred by the use of tigecycline as a first-line antibiotic versus potential savings obtained with such use, also in view of the prevention of possible treatment failures and the additional cost of administering a second course of antibiotic therapy, wherein the result would depend on the number of preventable treatment failures. The analysis concludes with a discussion and graphic illustrations comparing the differential probable treatment success which would render the two treatment alternatives economically indifferent.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".