PLD.53 Using clinical experience to elicit the components of a skilled and safe operative delivery
Bibliographic record
Abstract
Reduced working hours and increased caesarean delivery rates have resulted in less exposure to intricate deliveries by obstetrics trainees. Though trainees may not be exposed to these deliveries as part of their training, conversely they may be expected to be proficient in them once starting independent clinical practice. This study aims to identify the verbal and non-verbal components of three intricate deliveries – Kiellands, non-rotational and assisted breech. Labour and delivery nursing staff were asked to identify those clinicians who they considered to be particularly skilled in intricate deliveries. Those identified consistently were invited to participate in the study. With written consent participants were then videoed performing each type of delivery on a model in order to identify the verbal and non-verbal components of the delivery. Two clinicians reviewed videos. The initial summary was then circulated to all participants for their approval. Themes identified included the need for careful assessment of suitability, the role of the multidisciplinary team, need for careful and appropriate communication with the parents, the technique of delivery itself and postpartum care and documentation. Overall the clinicians balanced respect for the “elegant technique” of intricate deliveries with careful assessment and when to stop should safety criteria not be met. There is still a role for intricate deliveries in modern obstetric practice, and a need for good quality holistic training programs on how best to perform such deliveries. By identifying verbal and non-verbal components of skilled deliveries these can then be translated into an useful educational tool.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.125 | 0.023 |
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".