PO-0285 Identifying Radiologic Signs Of Life-threatening Causes Of Acute Upper Airway Obstruction In Children: Not That Easy!
Bibliographic record
Abstract
Background Soft tissue neck radiographs (STNRs) are viewed as a helpful tool to investigate causes of acute upper airway obstructions (AUAO) among children. Some radiological findings guide clinicians identifying life-threatening causes of AUAO. Do the clinicians are skilled enough to recognise those signs? Methods A retrospective study was conduct and the medical files from children aged 0–17 who had a STNR between January 1st2010 and December 31st 2011 in a Canadian university paediatric hospital were reviewed. Those performed in an AUAO context were analysed. Identification by clinicians of significant findings on STNRs related to life-threatening causes of AUAO were compared to radiologists’ reports. Kappa coefficients were determined to describe inter-rater agreement on SNRT findings. The frequency at which SNRTs were described as technically inadequate by radiologists was also analysed. Results Among 457 STNRs, 11% (n = 52) showed findings of life-threatening causes of AUAO according to clinicians, compared to 17% (n = 77) according to radiologists (global sensibility=35%,specificity=93%; kappa=0.327). Sensibility and specificity differed according to signs evaluated: 26% and 97%, kappa=0.27 for swollen epiglottis/aryepiglottic oedema (n = 23 according to radiologists); 37% and 96%,kappa=0.37 for swelling of the retropharyngeal space (n = 38); 0% and 99% kappa=-0.01 for decrease tracheal diameter/deviation (n = 16); 100% and 99%, kappa=0.86 for the presence of a foreign body (n = 3). Moreover, 15% of SNRTs were described as technically inadequate by radiologists. Conclusion Identification of life-threatening causes of AUAO on STNRs appears quite challenging for clinicians. SNRTs are technically difficult to perform among children. Importance of STNRs need to be reassessed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.006 |
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".