80: Modification & Field Testing of a Decision Aid & Decision Coaching for Counseling Parents Facing the Potential Birth of an Extremely Premature Infant
Bibliographic record
Abstract
Risk of death or neurodevelopmental impairment (NDI) is relatively high for extremely premature infants (EPI – 22–25 weeks GA). Given the notable prognostic uncertainty about the outcome, early intensive care and palliative care are both potentially acceptable options. Use of decision aids (DA) and decision coaching have been shown to improve decision quality and patient engagement in the decision making process. Although DAs have been evaluated in simulated antenatal counseling sessions for EPI, none have been tested during real life consultations. 1) To create a DA specific to our population; and 2) field test the DA and decision coaching in an at risk population. The only published EPI DA was assessed using the International Patient Decision Aids Standards (IPDAS) tool. An existing working group for EPI was surveyed to identify key elements to include in a DA and feedback was sought from the local family decision services team, neonatologists and parents. Four neonatologists were trained in decision coaching and alpha-tested the DA. The revised DA along with decision coaching was then field tested on women (and partners) at risk of delivering between 23+0 and 24+6 weeks GA. Usefulness for decision making and degree of pre/post decisional conflict were assessed. Deficits were identified in the published DA (IPDAS score 13/35): need for more information overall, incorporation of local data and creation of a detailed palliative care description. The EPI working group identified seven key elements essential for the DA: survival; moderate/severe NDI rates; quality of life of survivors and their parents; and maternal risk of death and long term morbidity. Revisions were made to the DA and to the number and content of the decision cards (3-options, 6-key elements and 16-GA specific data). Post-modification IPDAS score was 31/35. Ongoing field testing (8 parents to date) suggests: neonatologists like using the DA; average consult is 50 mins; DA presents balanced and clear information; DA reduces parental decisional conflict (22/40 down to 4/40). We were able to improve the quality of an existing yet untested DA using multi-source feedback. Field testing to date demonstrates our DA's promise for helping parents engage in the decision making process at the limit of viability.
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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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.008 |
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".