Social and Clinical Impact of Congenital Urological Malformations in a Developing Country: The Need for a Transdisciplinary Way of Treatment
Bibliographic record
Abstract
Introduction The prognosis of congenital anomalies (CAs) can be improved if detected and treated accurately. Given the complexity of some anomalies, it is almost always necessary to approach them with an interdisciplinary team. Our objective was to contact patients with congenital urological anomalies (CUAs) and follow them up during their first years of life and evaluate their clinical status, as well as their social and health care limitations. Method Based on the Bogota Congenital Malformations Surveillance Program (BCMSP), we have contacted by phone all the patients with CUAs and evaluated their follow-up. We have included all the registered patients from 2006 until 2015. A standardized questionnaire was applied by a trained staff. The questions assessed on each call included: evaluation of the clinical status of the patient, the clinical treatments and evaluations performed by clinical and surgical subspecialties, health care limitations, and social barriers. The first call was made at the 2nd month, then every 3 months during the 1st year and every 6 months thereafter. Results A total of 277 patients were contacted, 97.3% of whom have an increased risk of mortality or significant disability. The malformation related mortality was of 38.1%. Only 38.7% of the patients were evaluated by a specialist, while 57.4% where still waiting to be seen by a specialist. Ninety eight percent of the limitations related to the health care system were the long waiting lists to be seen by a specialist. Conclusion Many of the pathologies that we have found belong to the group that has a significant reduction in mortality when treated accurately and promptly. However, we have a profound problem in our health care system, in that many of the patients have not been seen by a specialist, which results in a worse prognosis and recovery rate.
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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 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".