R&D Implementation in a Department of Laboratory Medicine and Pathology: A Systematic Review Based on Pharmaceutical Companies
Bibliographic record
Abstract
A systematic literature review on pharmaceutical companies may be a tool for guiding some procedures of R&D implementation in a department of Laboratory Medicine and Pathology. The use of pharmaceutical companies for this specific analysis arises from less variability of standards than healthcare facilities. In this qualitative and quantitative analysis, we focused on three useful areas of implementation, including R&D productivity, commercialization strategies, and expenditures determinants of pharmaceutical companies. Studies and reports of online databases from 1965 to 2014 were reviewed according to specific search terms. Initially, 218 articles and reports were found and examined, but only 91 were considered appropriate and used for further analysis. We identified some suggested implementation strategies relevant for marketing to enhance companies' own R&D strategies; such as reliability of companies on "sourcing-in" R&D facilities and "think-tank" events. Regardless of the study and of the country, cash flow and profitability always positively influenced R&D expenditure, while sales and firm size did not. We consider that handling R&D determinants should require caution. It seems critical that implementation of R&D systems is directly related with productivity, if it reflects dual embodiment of efficiency and effectiveness. Scrutinizing the determinants of R&D expenditures emphasizes significant factors that are worth to highlight when planning an R&D investment strategy. Although there is no receipt fitting every situation, we think that health care plan makers may find relevant data in this systematic review in creating an initial implementation framework.
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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 | 0.023 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.020 | 0.025 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".