Patients' perspective of one-stop breast clinic, Lagos University Teaching Hospital
Bibliographic record
Abstract
Introduction: The complex nature of cancer diagnosis and treatment, with the pressing need for individualized patient care, has led to the services being organized into multidisciplinary teams (MDTs), also called tumor boards or cancer conferences. MDTs are beneficial as they provide coordinated, consistent, expert-driven, and cost-effective care that is delivered in a timely fashion to the patient. This study is aimed to assess the level of impact of a one-stop breast clinic on the management of breast cancer among breast cancer patients in Lagos University Teaching Hospital (LUTH). Methodology: A cross-sectional descriptive study was carried out among patients who attended the MDT breast clinic on referral from within and outside Lagos University Teaching Hospital LUTH. Results: The mean age ± standard deviation of the respondents was of 33.4 ± 7.62 years. More than half of the respondents (66%) felt satisfied about the workings of the MDT clinic, with less than a quarter of respondents reporting that were very satisfied with the clinic. Almost all the respondents (90%) were of the view that it allowed for a more expert opinion. Problems faced by the clinic in the MDT Clinic included filled up booking times (6%) and not taking enough time to attend to patients (2% each). Conclusion: The study revealed a good level of satisfaction among respondents about the MDT clinic; however, reservation on issues such as booking time, better patient to doctor relationship, and availability of more doctors were still of concern to patients. Addressing these issues are vital in achieving an all-round great experience in the multidisciplinary setting.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".