Lessons learned in applying the International Society for Pharmacoeconomics and Outcomes Research methodology to translating Canadian Emergency Department Information System Presenting Complaints List into German
Bibliographic record
Abstract
OBJECTIVES: The patient's presenting complaint guides diagnosis and treatment in the emergency department, but there is no classification system available in German. The Canadian Emergency Department Information System (CEDIS) Presenting Complaint List (PCL) is available only in English and French. As translation risks the altering of meaning, the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) has set guidelines to ensure translational accuracy. The aim of this paper is to describe our experiences of using the ISPOR guidelines to translate the CEDIS PCL into German. MATERIALS AND METHODS: The CEDIS PCL (version 3.0) was forward-translated and back-translated in accordance with the ISPOR guidelines using bilingual clinicians/translators and an occupationally mixed evaluation group that completed a self-developed questionnaire. RESULTS: The CEDIS PCL was forward-translated (four emergency physicians) and back-translated (three mixed translators). Back-translation uncovered eight PCL items requiring amendment. In total, 156 comments were received from 32 evaluators, six of which resulted in amendments. CONCLUSION: The ISPOR guidelines facilitated adaptation of a PCL into German, but the process required time, language skills and clinical knowledge. The current methodology may be applicable to translating the CEDIS PCL into other languages, with the aim of developing a harmonized, multilingual PCL.
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 | 0.416 | 0.383 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".