Linguistic History of Posterior Reversible Encephalopathy Syndrome: Mirror of Developing Knowledge
Bibliographic record
Abstract
BACKGROUND: the term posterior reversible encephalopathy syndrome (PRES) was first proposed in 2000. Since then, the acronym PRES has become very popular in imaging and clinical literature as it is short, easy to say and remember, and neatly couples the frequent localization of neuroimaging findings along with the typical outcome of this syndrome. Another possible reason for the popularity of this acronym in clinical circles is the connotation of PRES with (elevated blood) PRESsure, as a majority of cases are believed to be associated with hypertension. However, problems exist with the interpretation and common understanding of PRES, questioning the appropriateness of "P" and "R" in the acronym. The linguistic issues related to the acronym of PRES are interesting. OBJECTIVES: the aim of this work is to analyze the controversies related to the acronym of PRES. RESULTS: in 2006, modifying the meaning of the acronym was suggested, renaming it Potentially Reversible Encephalopathy Syndrome in order to adjust to the cases when posterior involvement is not prominent and emphasize that the reversibility is not spontaneous. This meant the creation of a backronym, where the new phrase is constructed by starting with an existing acronym. CONCLUSION: this new backronym indicates that the original acronym of PRES has become a misnomer.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".