Quality and accessibility of online patient self-education resources for breast reconstruction
Bibliographic record
Abstract
Background: With rising interest in breast reconstruction after mastectomy, patients are increasingly turning to online resources to supplement medical consultations. However, the quality and accessibility of these materials remain inconsistent. This study evaluates the readability, understandability, actionability, content coverage, and transparency of online breast reconstruction resources. Methods: The top 20 Google search results were examined for five common breast reconstruction-related queries. Metrics assessed included SMOG readability level, PEMAT scores (understandability and actionability), content coverage, and a modified EQIP score for quality. Statistical analyses examined relationships among these variables and with factors like search rank, author type, and query. Results: Mean content coverage was 49 %, with significant gaps in preoperative planning, treatment side effects, and fat grafting. Readability was poor (mean SMOG 12.3). Understandability was high (80 %), but actionability (37 %) and quality (modEQIP of 40 %) were low. Academic authors produced shorter and lower-quality resources. Higher-ranked resources were generally longer and correlated with better performance across most metrics. Specific queries like 'DIEP flap' yielded narrower, lower-quality resources. Conclusions: Online resources for breast reconstruction are highly variable and often fall short in readability, comprehensiveness, and transparency. Although understandability is generally acceptable, low actionability and inconsistent coverage hinder patient utility. Search engine rank modestly correlates with quality, suggesting some alignment between visibility and value. Improving these resources will require targeted efforts to simplify language, address topic gaps, and enhance actionable content-especially for specialized queries where quality remains lowest.
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 | 0.006 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".