Evaluation of the Clinical Maxim: “If It Ain't Broke, Don't Fix It”
Bibliographic record
Abstract
PURPOSE: A significant number of patients return to optometric practice dissatisfied with their spectacles. An important question is whether any of these cases are preventable. There are several different clinical maxims that are used to modify the subjective refraction when determining the refractive prescription. These maxims aim to improve patient comfort and adaptation and thereby reduce patient dissatisfaction with new spectacles. They are not based on research evidence, but rather on expert opinion gained from clinical experience. The aim of this study was to retrospectively analyze a large number of case records of dissatisfied patients to assess the possible usefulness of the prescribing maxim "if it ain't broke, don't fix it." METHODS: Three hundred eighteen non-tolerance cases from a university-based Canadian optometric clinic were categorized by a focus group of optometrists. Three prescribing categories were defined and comprised cases in which application of the proposed maxim may have prevented the recheck eye examination; a more limited application of the maxim for one working distance may have been appropriate; and finally scenarios in which the maxim did not work in that the practitioner was judged to have initially followed the maxim, yet patient dissatisfaction was still reported. The remaining unallocated records comprised prescribing situations outside the scope of this study. RESULTS: Approximately 32% of non-tolerance cases were judged to have been preventable by use of the proposed maxim. Furthermore, an additional 10% reduction in recheck cases may have been possible by a more liberal interpretation of the maxim. Conversely, 4% of cases were deemed to comprise scenarios in which the maxim was followed yet the patient returned later to report problems with their spectacles. CONCLUSIONS: The prescribing maxim "if it ain't broke, don't fix it" appears to have a role in reducing recheck eye examinations and improving patient satisfaction with new spectacles.
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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 | 0.003 | 0.014 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".