Diagnosis Discordance and Neonatal Transport: A Single-Center Retrospective Chart Review
Bibliographic record
Abstract
INTRODUCTION: In Canada, more than 4,000 critically ill newborns per year require transfer. Transports are initially managed based on information conveyed by referral practitioners. OBJECTIVES: To identify the frequency of diagnostic discordance between the referring facility, transport team, and tertiary care center in our outborn neonatal population and to verify the association between discordance events (DEs), prolonged transport stabilization times, and potential risk factors to further inform and facilitate the development of future outreach education initiatives. STUDY DESIGN: In this retrospective chart review, we identified and categorized DEs for patients transported by our service in a 1-year period. Associations between DE, transport stabilization times, and patient variables were studied using univariate and multivariable approaches. RESULTS: From 233 eligible patients, 10.7% of patients had referral to discharge discordance events. No significant association was identified between stabilization time and DE. Birth weight and presence of a neurologic diagnosis were associated with DE. CONCLUSION: Diagnostic discordance was identified in 1 of every 10 neonates transported and found to be associated with patients with higher birth weight and the presence of neurologic diagnoses. Outreach initiatives will be developed and adapted accordingly, with a focus on this population.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".