Waits for Surgery Following Hip Fracture
Bibliographic record
Abstract
Almost all hip fracture patients undergo surgery to repair the fracture.Recent research suggests that timely repair is important for good outcomes following surgery.Patients who had surgical repair of a hip fracture in 2003-2004 were identified using hospitalization data collected by the Canadian Institute for Health Information.Time to surgery was calculated from day of admission to day of surgery.The associations of DATA M AT T E R SHEALTHCARE POLICY Vol.2 No.1, 2006 [37] both patient and system characteristics with waits for surgery were considered.While the majority of patients had surgery on the day of or the day following admission, 29% waited two days or longer for surgery.Wait times were related to patients' age, hospital size, day of admission and whether patients were transferred. RésuméPresque toutes les personnes victimes d'une fracture de la hanche subissent une chirurgie pour réparer la lésion.De récents travaux de recherche suggèrent qu'une intervention en temps opportun est importante pour assurer de bons résultats après la chirurgie.On a repéré les patients qui ont subi une intervention chirurgicale après une fracture de la hanche en 2003/2004 à l' aide des données sur l'hospitalisation compilées par l'Institut canadien d'information sur la santé.On a ensuite calculé le délai entre la date d' admission et celle de la chirurgie.On a aussi tenu compte de l' association entre les caractéristiques des patients et du système et les temps d' attente pour la chirurgie.Tandis que la majorité des patients ont été opérés le jour même de leur admission à l'hôpital ou le lendemain, 29 % ont attendu deux jours ou plus pour se faire opérer.Les temps d' attente variaient selon l' âge des patients, la taille de l'hôpital, le jour de l' admission et si les patients avaient été transférés ou non.T
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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".