Task shifting in primary eye care: how sensitive and specific are common signs and symptoms to predict conditions requiring referral to specialist eye personnel?
Bibliographic record
Abstract
BACKGROUND: The inclusion of primary eye care (PEC) in the scope of services provided by general primary health care (PHC) workers is a 'task shifting' strategy to help increase access to eye care in Africa. PEC training, in theory, teaches PHC workers to recognize specific symptoms and signs and to treat or refer according to these. We tested the sensitivity of these symptoms and signs at identifying significant eye pathology. METHODS: Specialized eye care personnel in three African countries evaluated specific symptoms and signs, using a torch alone, in patients who presented to eye clinics. Following this, they conducted a more thorough examination necessary to make a definite diagnosis and manage the patient. The sensitivities and specificities of the symptoms and signs for identifying eyes with conditions requiring referral or threatening sight were calculated. RESULTS: Sensitivities of individual symptoms and signs to detect sight threatening pathology ranged from 6.0% to 55.1%; specificities ranged from 8.6 to 98.9. Using a combination of symptoms or signs increased the sensitivity to 80.8 but specificity was 53.2. CONCLUSIONS: In this study, the sensitivity and specificity of commonly used symptoms and signs were too low to be useful in guiding PHC workers to accurately identify and refer patients with eye complaints. This raises the question of whether this task shifting strategy is likely to contribute to reducing visual loss or to providing an acceptable quality service.
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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 | 0.010 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".