DAILY COST PREDICTION MODEL IN NEONATAL INTENSIVE CARE
Bibliographic record
Abstract
OBJECTIVES: One barrier to economic evaluation alongside neonatal randomized controlled trials is the expense of collecting detailed patient resource information. To reduce this data collection burden, we identified the key resource items that predict daily ancillary costs for extremely low birth weight infants. METHODS: Participants were 385 infants enrolled in the Trial of Indomethacin Prophylaxis for Preterms in nine tertiary level neonatal intensive care units in Canada. Information on eighty-nine nonpersonnel resource items was abstracted from the hospital chart from admission to tertiary hospital discharge. Unit costs were derived from a provincially standardized cost accounting system. Using stepwise linear regression, models correlating total daily ancillary costs with key resource items were constructed for each of five periods of admission. Models were derived in a randomly split half of the total sample of patient days and validated against the remainder. RESULTS: The 385 infants contributed resource information from 23,354 admission days. The regression model for weeks one to twelve included the covariates surfactant, chest radiograph, red blood cell transfusion, cranial ultrasound, abdominal radiograph, parenteral amino acid infusion, surgery, platelet transfusion, and echocardiogram and explained 91% of the variability in daily nonpersonnel costs (P<.0001). Models for other admission periods similarly included between four and eight covariates, were highly significant (P<.0001) and explained between 76% and 94% of daily ancillary cost variability. The regression equations showed excellent predictive power when applied to the second half of the patient data set. CONCLUSIONS: Daily nonpersonnel costs for extremely low birth weight infants are driven by a limited number of key resource variables. The ability to predict total ancillary costs with minimal data collection will facilitate inclusion of economic evaluations in neonatal trials.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".