Analysis of reporting system for procedural errors in the endodontics departments of dental institutes of Punjab
Bibliographic record
Abstract
<p><strong>Background and Objective:</strong> Procedural error reporting system is an effective step towards patient safety. In Pakistan adverse event reporting system is deficient. The study aimed to identify the presence of procedural error reporting system and their referral in endodontic clinics of private and public dental institutes in Punjab.</p> <p><strong>Methods:</strong> This study was conducted in six private and two public dental institutes in Punjab via questionnaires developed by the authors and distributed online using Google forms. The questionnaire had 14 items targeting demographics, a procedural error reporting system, and ways to manage procedural errors.</p> <p><strong>Results: </strong>The majority of the dental colleges, both private and public, did not have any procedural error reporting system or a hierarchical order (70.1%) for the management of errors in their endodontic departments (60.5%). The most frequent approach was self-management of errors (86.5%), followed by referring to a senior endodontist (45%) and asking colleagues for help (36.5%). 13% of the respondents never informed patients, while 6% never reported procedural errors to their departments. There was a statistically significant difference between house officers, postgraduate trainees and demonstrators regarding self-management of errors and putting patients on follow-up.</p> <p><strong>Conclusion:</strong> Most endodontic departments lack a system for reporting procedural errors, with no significant difference between private and public institutes. Most respondents report errors to patients and departments, with self-management being the most common approach. Postgraduate trainees tend to manage errors independently. It is imperative to create a comprehensive error reporting system that could be implemented progressively</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".