Bibliographic record
Abstract
Objectives: To accelerate, facilitate and simplify the transport of oral syringes produced in emergency at a far end from the pharmacy to their validation area of the Manufacturing sector. In context : Certain seemingly simple problems can significantly slow down the efficiency of a pharmacy department. For example, the remoteness of the oral syringe production room from the post of the pharmacist responsible for checking them can cause delays in verification and shipping by the simple fact that the technical assistants responsible for producing these syringes wait, either '' have an appreciable amount to carry, either when the break or dinner time has come, since they have to go there. Different solutions can be envisaged, such as a pneumatic transport system or an internal conveyor in the pharmacy department. However, these systems are expensive and difficult to justify for a department in the process of being renovated within two or three years, Results: A simple, inexpensive and temporary solution was implemented, namely the use of a LEGOMD robot equipped with an intelligent Mindstorms® robot type brick. The use of this sophisticated toy succeeds in simplifying the work and avoids many trips to different staff members. Conclusion: The addition of this robot, which at the base is only a toy, has made it possible to reduce the travel of technical personnel and indirectly speed up the delivery of urgent preparations of oral syringes for patients. It is an interesting application of a simple pharmaceutical transport solution. Objectives: To expedite, facilitate and streamline the transport of urgently prepared oral syringes from a far end of the pharmacy to the area in the production sector where they are checked. Background: Certain simple problems can impede a pharmacy department’s efficiency considerably. For example, if the area where oral syringes are prepared is far from the workstation of the pharmacist responsible for checking them, this can result in delays in verification and shipping simply because the technical assistants responsible for preparing the syringes wait until they have a considerable number of them to transport or for their break or lunch, as they have to go by the workstation in question. Different solutions can be considered, such as a pneumatic transport system or an internal conveyor within the pharmacy department. However, such systems are expensive and difficult to justify for a department that is in the process of being renovated within the next 2 or 3 years, as the planning and construction of a new pharmacy are already underway. Results: A simple, inexpensive, temporary solution was implemented, namely, using a LEGO® robot equipped with a Mindstorms® robot intelligent brick. This sophisticated toy streamlines the work and saves different staff members many trips. Conclusion: The addition of this robot, which is basically only a toy, has reduced the number of trips by technical staff and indirectly speed up the delivery of urgently prepared oral syringes for patients. This is an interesting application of a simple pharmaceutical transport solution.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.036 | 0.028 |
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".