Fatores de Risco Associados à Recusa de Notas de Transferência e Vales Cirurgia: O Caso da Região Centro em Portugal
Bibliographic record
Abstract
INTRODUCTION: In Portugal, the rate of refusals regarding transfer between hospitals through surgery vouchers is high, which makes it difficult to meet maximum waiting times for elective surgeries. The objectives of this study are to examine how many vouchers were issued and refused between the third quarter of 2016 and the fourth quarter of 2019 and the risk factors associated with their refusal, in Central Portugal Material and Methods: Data was obtained in the database of cancelled vouchers and the waiting list for surgery on the 31st December 2019. Multiple logistic regression was used to investigate risk factors. RESULTS: The number of issued vouchers increased after 2018 and the rate of refusals has been above 55% since the 3rd quarter of 2018. Refusal was more likely for individuals aged 55 years or above (OR = 1.136; CI = 1.041 - 1.240; OR = 1.095; CI = 1.005 - 1.194; OR = 1.098; CI = 1.002 - 1.203, for the age bands 55 - 64, 65 - 74 and 75 - 84, respectively), for inpatient surgery when compared to ambulatory (OR = 2.498; CI = 2.343 - 2.663) and for Orthopaedics when compared to General Surgery (OR = 1.123; CI = 1.037 - 1.217). The odds of refusal also varied across hospitals (for example OR = 3.853; CI = 3.610 - 4.113; OR = 3.600; CI = 3.171 - 4.087; OR = 2.751; CI =3.383 - 3.175 e OR = 1.337; CI = 1.092 - 1.637, for hospitals identified as HO_2, HO_7, HO_4 and HO_6, respectively). CONCLUSION: In this study, we have confirmed that the number of issued surgery vouchers increased after the administrative reduction of maximum waiting times in 2018 and that the rate of transfer refusals has been increasing since 2016 and has remained above 55% from the third trimester of 2018 onwards. Some of the factors for which we obtained a positive association with refusal are age, inpatient surgery (compared to ambulatory) and Orthopaedics (compared to General Surgery).
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".