General Session 4: Acute Care 1
Bibliographic record
Abstract
Objective: Analysis of unique factors in the pregnant spinal cord injury patient.Design/Method: A cohort of pregnant spinal cord injury (SCI) patients from the Texas Institute of Rehabilitation and Research (TIRR) were analyzed.Results: Sixty-two SCI patients were studied; 12 were pregnant.This is a 19.3% pregnant rate.The specific injuries of the pregnant patients were T1, T3, T4-5, T9, T12, L5-51, C5, C5-6, C6 tetra.One patient was T4-T5; it was her second pregnancy post injury.All the patients received epidural anesthesia.The patients had 12 live births: 2 by repeat cesarean section, 7 normal vaginal delivery, and 3 mid forceps vaginal deliveries.This is an 83% vaginal delivery rate.Factors involved in these patients included: A. Counseling of reproductive options including pregnancy.Many patients have been told that they could not or should not become pregnant.B. Pregnancy medical management of spasm and reflexes that change with the expanding gravid uterus and monitoring for atypical signs of labor.C. Intrapartum management with epidural anesthesia for autonomic dysreflexia.D. Use of mid forceps when patient was unable to push.The Salinas spoons used for the assistance of the second stage were optimum for mother and infant.E. Adaptive physical and occupational therapy for postpartum maternal activated for lifting, diapering, swaddling, and other infant care activities.Conclusion: SCI patients can have successful pregnancies despite their injury and should have reproductive options.Practitioners should be aware of the need for atypical monitoring of pain including labor.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.594 | 0.246 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".