Diagnostic Support for the Surgical Patient: The Experiences and Challenges, As Seen by Practitioners in Resource-Poor Setting
Bibliographic record
Abstract
Background: The input of laboratory medicine has no doubt improved surgical practice and will continue to impact positively on patient care. The aim of this study was to explore the experiences of practitioners and diagnostic challenges if any, encountered in the care of surgical patients in Port Harcourt in the last quarter of year 2022. Materials and Methods: A descriptive observational study was carried out among total population of consenting health workers (medical doctors, laboratory scientists / technologists, and technicians) in the Surgery and Diagnostic Services Departments in two teaching hospitals in Port Harcourt, using self-administered questionnaires. Data on experiences and challenges was analysed using the Statistical Package for Social Sciences (SPSS) version 20.0. Results: The respondents had a male to female ratio of 1.3:1, mean age of 35.47 ± 8.44 years, mean years in practice of 7.58 ± 6.97 years, and 171 (98.3%) were Christians. One hundred and sixteen (66.7%) respondents were aware of delay in diagnostic services, in varying degrees. Lack of reagents (49 = 28.2%), inadequate personnel (18 = 10.3%), long processing time (15 = 8.6%) and poor electric power supply (9 = 5.2%) were the most common reasons for delay in diagnostic test results. Diagnostic challenges were highlighted, occurrence of medico-legal issues was reported, and solutions proffered. Conclusion: The professionals practicing in the diagnostic / surgical departments were aware and do experience delays in diagnostic test results and errors (reported by a few) that affects surgical services in our environment. Their experiences and challenges were highlighted and recommendations were made.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".