Outcomes among children with hearing loss admitted with acute lymphocytic leukemia.
Bibliographic record
Abstract
e22000 Background: Hearing loss among children can influence their growth and development. It can also influence how they seek help for any significant health-related complications. While some studies have highlighted such issues in the adult population, there needs to be more data on pediatric hospitalizations. We, therefore, attempted to conduct a retrospective analysis among Acute lymphocytic leukemia (ALL) cases in children with a diagnosis of hearing loss. Methods: Children below 18 were recruited from the 2016-2020 National Inpatient Sample (NIS) provided by the Healthcare Cost and Utilization Project. The diagnoses of ALL and hearing loss (of any form) were found using the International Classification of Disease 10th Revision (ICD-10) codes. We evaluated the presence of hearing loss in various patient groups and compared different potential complications of ALL between children with hearing loss (vs. those without). Results: We found 123430 weighted cases of ALL among children of ages 0-17 (inclusive) in the United States. Nine hundred fifty-five children (0.8%) also reported a diagnosis of hearing loss. Such diagnosis was more likely in patients who were Hispanics (vs. White, aOR1.458, 95% CI 1.247-1.703, p < 0.01) or of ages 11-17( vs. ages 0-10, aOR 1.920, 95% CI 1.679-2.196, p < 0.01). Meanwhile, children with ALL and hearing loss are less likely to be covered by Private Insurance (vs. Medicaid, aOR 0.811, 95% CI 0.699-0.941, p < 0.01), and less likely to be females (aOR 0.469, 95% CI 0.402-0.547, p < 0.01). Moreover, the presence of a diagnosis of hearing loss among ALL patients was also associated with a higher odd of septicemia (aOR 2.211, 95% CI 1.797-2.721, p < 0.01), use of palliative care (aOR 3.111, 95% CI 2.252-4.299, p < 0.01), and mortality (aOR 2.604, 95% CI 1.659-4.087, p < 0.01). Conclusions: Our analysis provides a novel perspective on the characteristics of hearing loss patients with ALL. Racial and socio-economic differences were confirmed. Furthermore, as they have higher odds of being under palliative care, they may also be sicker, which explains the higher odds of complications such as septicemia and mortality during hospitalization. Further studies must be done to understand the disparities seen in our study in a more clinical setting and address them appropriately.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".