Use of special health care services by infants born extremely prematurely in the province of Quebec
Bibliographic record
Abstract
To compare health care use from neonatal discharge to 18 months corrected age of two groups of extremely preterm children (< 26 vs. 26-29 weeks of gestation), we used a province-wide database containing neonatal and follow-up data on 254 infants (77% of survivors) born at < 29 weeks of gestation and cared for at 3/6 neonatal units in Quebec in 2003-2004. Neonatal data were abstracted from medical records by trained personnel. At 18 months corrected age, neurodevelopmental status was assessed by psychologists and paediatricians. Data on health care use were collected from charts and parent interviews. Descriptive statistics are provided and logistic regression analysis was carried out to evaluate perinatal and social determinants of re-hospitalization and frequent use of health services resources. Results show that 57% of infants born at < 26 weeks (n=49) and 49% of those born at 26-29 weeks (n=205) were re-hospitalized, mostly for respiratory illness. Both groups used a significant amount of health resources: 61% vs. 59%, respectively, received physical or occupational therapy, 29% vs. 17%, respectively, required long-term rehabilitation, 38% vs. 28%, respectively, used prescribed medication, and 59% vs. 33%, respectively, required home medical equipment (home oxygen, apnea monitors, orthopaedic devices and visual aids). Risk of re-hospitalization was associated with severe brain injury, use of an apnea monitor, and older age at neonatal discharge. Multiple birth, severe brain injury, suspected neonatal sepsis, and single-parent household were independently associated with the risk of using health care services above average. These results highlight the importance of resource allocation to preterm infants for medical and rehabilitation services after discharge from the neonatal intensive care unit.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".