Considering the Language of Computerized Order Entry Systems
Bibliographic record
Abstract
Computerized Provider Order Entry (CPOE) systems have been shown to introduce new problems into clinical environments. Given the communication intensive nature of these systems considering the language(s) of communication can provide insight into their function and subsequent problems. The current (as November 2015) CPOE literature was reviewed using the language concepts of syntax, semantics, and pragmatics as a lens. In total, 202 articles were considered, of these only 46 received a full review. 145 results related to language concepts were extracted from these articles. These were categorized into five categories: syntax, semantics, system-pragmatics, syntax-pragmatics, and semantic-pragmatics. In total key themes were synthesized. The themes identified can be used to direct further research in the area of CPOE systems. It was found that current literature heavily favors pragmatics concerns of language at the expense of considering underlying factors (syntax and semantics). The results support the use of language as a means of analyzing interactions between actors in communication intensive systems.
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 | 0.016 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".