Predicting the clinical course of suspected acute viral upper respiratory tract infection in children
Bibliographic record
Abstract
BACKGROUND: Suspected acute viral upper respiratory tract infection (SAVURTI) is the commonest acute reason why children consult in general practice. The clinical course varies widely and about one in five children re-consult for the same SAVURTI episode. If clinicians had feasible tools for predicting which children are likely to suffer a prolonged course, then additional explanations and possibly treatments could be provided at the initial consultation that might enable carers to manage the condition without re-consulting. OBJECTIVE: To identify features available on the day of consulting that might predict a prolonged clinical course among children with SAVURTI. METHOD: Regression analysis using Canadian Respiratory Illness and Flu Scale (CARIFS) data from a randomized controlled trial cohort of children aged from 6 months to 12 years consulting in general practice with SAVURTI. RESULTS: Two variables from the clinician's records ('age' and 'cough') and two variables from the CARIFS completed by carers on the day of consulting ('fever' and 'low energy, tired') explained approximately 15% of the variation present in CARIFS scores on day seven. CONCLUSION: Children and carers may benefit from a clear account of the evidence that the clinical course of RTIs in children varies widely and may be longer that expected, and that prediction for individuals is difficult.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".