Development and an initial validation of the Responses to Illness Severity Quantification (RISQ) score for severely malnourished children.
Bibliographic record
Abstract
AIM: To develop and perform an initial validation of a score to measure the severity of illness in hospitalised children with severe acute malnutrition (SAM). METHODS: A prospective study enrolled SAM children aged 6-59 months hospitalised in Borno State, Nigeria. Candidate items associated with inpatient mortality were combined and evaluated as candidate scores. Clinical and statistical methods were used to identify a preferred score. RESULTS: The 513 children enrolled had a mean age of 15.6 months of whom 48 (9%) died. Seven of the 10 evaluated items were significantly associated with mortality. Five different candidate scores were tested. The final score, Responses to Illness Severity Quantification (RISQ), included seven items: heart rate, respiratory rate, respiratory effort, oxygen saturation, oxygen delivery, temperature and level of consciousness. The mean RISQ score on admission was 2.6 in hospital survivors and 7.3 for children dying <48 h. RISQ scores <24 h before death had an area under the receiver operating characteristic curve (AUROC) of 0.93. The RISQ score performed similarly across differing clinical conditions with AUROCs 0.77-0.98 for all conditions except oedema. CONCLUSION: The RISQ score can identify high-risk malnourished children at and during hospital admission. Clinical application may help prioritise care and potentially improve survival.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".