192 The positive impact on postnatal metrics – relocation of the elective caesarean section pathway to the 8th floor, gynaecology ward
Bibliographic record
Abstract
The COVID-19 pandemic profoundly affected clinicians’ work patterns and the clinical activity within the hospital. In the Maternity Department at St Thomas’s hospital in London, COVID-19 positive patients were cared for separately on Hospital Birth Centre and cancellation of elective theatre lists led to empty theatres and under-utilised surgical wards. Over a 6 week period, we looked at the relocation of the low risk elective caesarean section (ELCS) pathway or ‘green’ according to a devised Traffic light Criteria, to a separate floor and evaluated the impact on relevant process measures. This involved strong leadership and multidisciplinary team involvement to ensure that the new pathway was integrated smoothly, without disruption to patient care. We compared our data related to this pathway to baseline data from previous work on the ELCS pathway and discussed the pandemic changes and how this created an opportunity to address challenges. We evaluated patient and staff experience using specifically designed questionnaires. On 56% of the days, the average time to discharge was less than 36 hours compared to 48 hours prior to relocation during COVID-19. 67% of cases were completed in less than 45 minutes. On 33% of days during the relocation period, all cases were in theatre before 09:15am compared to 20% prior to relocation. There were no HDU admissions and 7 postpartum haemorrhages (EBL <1000mls). Overall positive patient feedback was obtained from the 17 completed questionnaires during the relocation. However, only 65% of women felt they were given adequate information about the birth of their baby and 53% about their postnatal recovery. 50% of trainees felt that their learning experience had improved with respect to performing ELCS. This work has shown that challenges like the COVID-19 pandemic can present opportunities for innovative solutions to be developed and implemented at short notice.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".