103 ORTHOGERIATRIC SERVICES IN THE FACE OF COVID-19
Bibliographic record
Abstract
Abstract Background Nationally agreed standards improve the level of care delivered to all older, frail, multi-morbid patients presenting with hip fractures. Dedicated Orthogeriatric services allow for these standards to be achieved in a multi-disciplinary team (MDT) setting. As the COVID-19 pandemic reached our shores, the model of care set out by the Irish Hip Fracture Standards (IHFS) was under threat. Our dedicated Orthopaedic Trauma ward became an acute COVID ward and the Orthogeriatric service was re-deployed to acute medicine for Quarter 2. Methods Using the Irish Hip Fracture Database, local data was analysed and compared with national data from Quarter 1 to 4 (Q1–4) in 2020. Results When comparing local IHFS’s with national figures, ongoing challenges and future goals are highlighted. In 2020, there were 222 hip fracture patients (mean age 81.8 years) in our hospital. Standard 1, time to the ward <4 hours, stands at 71% locally (national average 33%). Standard 2, time to theatre <48 hours, is an ongoing challenge and remains at 66% (national average 75%). Standard 3, pressure ulcer rate, was the same as the national average at 3%. Standards 4, 5 and 6 in our hospital stand at 87% (national averages of 82%, 91% and 85% respectively). In Q1, 56%, or over 1 in every 2 patients with hip fractures, met all of the Irish Hip Fracture Standards in our hospital. In Q2, only 18% of patients met all of the IHFS’s. Q3 saw improvements with 47% of all hip fracture patients achieving all IHFS’s. Q4 showed maintenance with 45% of all patients achieving all IHFS’s. Conclusion These findings highlight the need for a dedicated Orthogeriatric Service and Orthopaedic ward at all times. Going forward with the risk of future waves and the emergence of new variants, every effort should be made to maintain a comprehensive orthogeriatric service to minimise a negative impact on patient care.
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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".