Delayed diagnosis: An important prognostic factor for oesophageal atresia in developing countries
Bibliographic record
Abstract
AIM: The aim of this study is to analyse the effect of delayed diagnosis on mortality rates, and evaluate the role of delayed diagnosis as a new prognostic factor in patients with oesophageal atresia (OA), especially in developing countries. METHODS: The records of 80 consecutive patients with OA (2008-2013) were reviewed. Patients were divided into two groups according to the time of diagnosis. As we demonstrated the effect of delayed diagnosis on mortality, we decided to develop a new classification that will be utilised to predict the prognosis of OA. The discrimination ability of the new prognostic classification was compared with those of the Waterston, Montreal and Spitz classifications using the area under the curve. RESULTS: The parameters of the new prognostic classification were birth weight less than 2000 g, the presence of major cardiac/life-threatening anomalies and delay in diagnosis. Class I consisted of patients with none or one of these parameters. Class II consisted of patients with two or three of these parameters. The area under the curve of the new classification was better than those of the other classifications in determining the prognosis of patients with OA. CONCLUSIONS: Delayed diagnosis of OA significantly led to morbidity and mortality. Although delayed diagnosis is not a characteristic of newborn or a marker of severity for OA and is a health care system issue in developing countries, we here point out that it is a prognostic factor in its own right. Our new classification has a superior discriminatory ability compared to the above-mentioned classifications.
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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 | 0.001 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".