Pharmaceutical Management for Patients: Calculation of Administration Fees for Intensive Care Unit
Bibliographic record
Abstract
In April 2008,the Japanese government introduced a revised fee schedule for medical services.We report on pharmaceutical management for calculating administration fees under the revised schedule for the intensive care unit (ICU).We prepared a special form for standardizing pharmaceutical management for the ICU and its usefulness was evaluated through an interview survey of physicians.It was found that in the six-month period after the revised fee schedule was introduced,changes in prescriptions were necessary for 31 items in 94 cases (total interventions : 564 cases) for the ICU and 31 items in 38 cases (total interventions : 359 cases) for the coronary care unit (CCU).The physicians were also questioned on the benefits of pharmacist participation in intensive care management.Many physicians in the ICU said that it was useful to be able to inquire about a patient's history of adverse effects and their frequencies and many in the CCU said that it enabled them to check patients' drug histories as well as the drugs that they brought to hospital easily.In both units,physicians desired information on proper doses of anti-MRSA agents and other antibiotics.The ICU management form we made was considered useful in that it allowed physicians to know about time-lapse circulatory dynamics and laboratory test results.Such information is necessary for pharmacotherapy used in the management of cardiovascular diseases.The form also provided documentation for drugs frequently requested by physicians and those requiring particular caution.
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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 | 0.010 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".