How Do I Forestall Platelet Stockpiling? Experience from a Tertiary Care Center
Bibliographic record
Abstract
Background: Among all the blood products, platelets had been reported to have a high rate of outdates due to its unpredictable demand and short shelf life of only 5 days. Researchers have applied techniques of management science and inventory theory to develop a model for inventory management. However, they failed to be implemented due to the variations in the demand and supply and complex computational models. Aims: To analyze the utilization pattern of platelet concentrates and discuss the method of optimal inventory management. Methods: We conducted a prospective observational study on platelet inventory practice at our center from January to December 2014. The number of units to be prepared is decided on daily basis by the transfusion medicine faculty or the resident. The utilization, wastage, expiry and the day’s cover are calculated for the study period. Future requirement is estimated based on the usage in the previous quarter, discard rare, average increase in usage and an additional 1% for managing disasters. Results: During this period a total of 6241 and 5706 units of platelet concentrates were prepared and issued respectively. The wastage rate was 5.1% and expiry rate was 3.5%. The average day’s cover of platelet units at our center was found to be 3 days using average monthly stock available and issued platelets. We observed that holding a stock of 45 units of platelets per day we had a cover for about 3 days for issue. Calculation of future requirement(6309) gave a high prediction when compared to the actual platelets prepared(6241). Conclusions: Understanding and regularly monitoring the inventory, setting up an optimum inventory level, follow of first in first out policy and to have an alternate management plan in times of shortage, like usage of apheresis products are some of the strategies which would benefit in best inventory practices.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".