Documentation during neonatal resuscitation: a systematic review
Bibliographic record
Abstract
OBJECTIVE: Accurate documentation in healthcare is necessary for ethical, legal, research and quality improvement purposes. In this review, we aimed to evaluate the accuracy of methods of documentation of delivery room resuscitations. METHODS: A systematic literature search in MEDLINE was conducted to identify original studies that reported the quality of documentation records during newborn resuscitation in the delivery room. Data extracted from the studies included population characteristics, methodology, documentation protocols, use of gold standard and main results (initial assessment of heart rate and peripheral oxygen saturation, respiratory support and supplementary oxygen). RESULTS: In total, 197 records were screened after initial database search, of which seven studies met the inclusion criteria and were finally included in this review. Four studies were chart reviews and three studies compared conventional documentation methods with video recording. Only one study tested an intervention to improve documentation. Documentation was often inaccurate and important resuscitation events and interventions were poorly recorded. Lack of uniformity among studies preclude pooled analysis, but it seems that complex or advanced procedures were more accurately reported than basic interventions. CONCLUSIONS: There is little literature regarding accuracy of documentation during neonatal resuscitation, but current quality of documentation seems to be unsatisfactory. There is a need for consensus guidelines and innovative solutions in newborn resuscitation documentation.
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 | 0.016 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".