Translating the Symptom Screening in Pediatrics Tool (SSPedi) into Argentinian Spanish for paediatric patients receiving cancer treatments, and evaluating understandability and cultural relevance in a multiple-phase descriptive study
Bibliographic record
Abstract
OBJECTIVES: To translate a symptom screening tool developed for paediatric patients receiving cancer therapies called Symptom Screening in Pediatrics Tool (SSPedi) into Argentinian Spanish and to evaluate the understandability and cultural relevance of the translated version of SSPedi among children with cancer and paediatric haematopoietic stem cell transplant (HSCT) recipients. METHODS: We conducted a multiphase, descriptive study to translate SSPedi into Argentinian Spanish. Using two translators, forward and backward translations were performed. The translated version was evaluated by Spanish-speaking paediatric patients 8-18 years of age receiving cancer treatments in two centres in Argentina and El Salvador. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome was patient self-reported difficulty with understanding of the SSPedi instructions and each symptom using a 5-point Likert scale. Secondary outcomes were incorrect understanding of the SSPedi instructions, symptoms and response scale determined by cognitive interviews with the patients and rated using a 4-point Likert scale. Cultural relevance was assessed qualitatively. RESULTS: There were 30 children enrolled and included in cognitive interviews; 16 lived in Argentina and 14 lived in El Salvador. The most common types of Spanish spoken were Central American (17, 57%) followed by South American (10, 33%) and Castilian (3, 10%). No changes to Argentinian Spanish SSPedi were required based on the outcomes or qualitative comments. No issues with cultural relevance were identified by any of the respondents. CONCLUSIONS: We translated and finalised Argentinian Spanish SSPedi. Future research will focus on its use to describe bothersome symptoms by Argentinian Spanish-speaking children.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".