241 THE EFFECT OF EARLY CLINICAL INTERVENTION ON NEONATAL ADMISSION ILLNESS SEVERITY.
Bibliographic record
Abstract
Preamble The Transport Risk Index of Physiologic Stability (TRIPS) is an additive measure of instantaneous physiologic stability of the infant before and after transport. The Neonatal Therapeutic Intervention Scoring System (NTISS) is an additive measure of treatment intensity. The Score for Neonatal Acute Physiology (SNAP-II) is an additive, physiology-based illness severity score calculated 12 hours following admission. Purpose To determine how the treatment intensity (NTISS) affects the measurement of the illness severity (SNAP-II) following admission to the neonatal intensity care unit (NICU). Methods The outcomes of 5,662 transported neonatal admissions from 17 tertiary referral perinatal centers were analyzed from January 1996 to October 1997. Post-transport TRIPS scores, SNAP-II scores, and NTISS scores were calculated. The post-transport TRIPS scores were arbitrarily stratified into five equal groups. For a given post-transport group (TRIPS), the treatment intensity (NTISS) was divided into quartiles. Descriptive statistics were used to determine whether high treatment intensity measured by NTISS reduces the mean SNAP-II illness severity score for a given post-transport TRIPS group. Results The treatment intensity after NICU admission was found to be correlated with the SNAP-II score (Pearson correlation 0.60). Thus, a high NTISS score is correlated with high SNAP-II score. This result contradicts our initial hypothesis that a higher treatment intensity (NTISS) would reduce the illness severity score (SNAP-II). Of note, the patients within each post-TRIPS group had mean post-TRIPS values that were not statistically different. Hence, the different treatment intensities (NTISS) within a given post-TRIPS category are comparable. Conclusions The use of SNAP-II to measure illness severity may introduce treatment contamination since it is measured over a 12-hour period. TRIPS may solve the problems of SNAP-II since it is an instantaneous measurement of illness severity. Thus, it is possible that treatment intensity may affect outcomes, that is, providing more treatment may not necessarily result in a more favorable outcome. In order to determine whether high treatment intensity (NTISS) is the cause of high illness severity (SNAP-II), we must determine whether TRIPS can overcome the inadequacies of SNAP-II. Hopefully, with further research, this may be accomplished by validating a TRIPS score at 12 hours post-admission to the NICU.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".