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Purpose <p>We analyzed plastic surgery applicants in the Texas Seeking Transparency In Application to Residency (STAR) database (2021–24) to assess how number of interview offers (NIO) relates to match outcomes and how this may affect signaling strategy.</p> Methods <p>Multivariable logistic regression compared NIO with other objective factors such as Step scores, and regression models were fit for NIO vs. match probability. Applicants were stratified as pre-signal implementation (2021–2022) or post-signal (2023–2024).</p> Results <p>248 applicants were included, of which 182 (73.4%) matched. The median NIO was 12 ± 9.5, and NIO significantly predicted matching (p &lt; 0.01). The maximum probability of matching (89.3% match probability) was predicted at 26.6 NIO. Applicants with less than 12 NIO had significantly lower match rates (60.4%) than those with 12–27 NIO (82.9%) or greater than 27 NIO (88.5%) (p &lt; 0.01). In the &lt; 12 NIO group, no variables distinguished matched from unmatched applicants. Median NIO (p = 0.08) and mean NIO of matched applicants (p = 0.09) were similar pre-signal and post-signal. However, the impact of NIO on match chances significantly differed between pre-signal and post-signal groups by Z-statistic (p &lt; 0.01), with NIO predictive of successful match only in the post-signal group (OR 1.31 [1.13, 1.51], p &lt; 0.01). The minimum NIO for 90% chance of matching was 47.5 in the pre-signal group and 14.5 in the post-signal group.</p> Conclusions <p>The implementation of preference signaling may increase the importance of each interview and alter NIO thresholds for a successful match. Our findings suggest increasing plastic surgery signals to 25–30 may promote thoughtful applications while maintaining strong match outcomes.</p>

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Interview offer thresholds and match success: Optimizing signal strategy in the integrated plastic surgery match

  • 期刊论文

  • 发表日期：

    2026年3月3日

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  • Elaine Lin,
  • Joey Liang,
  • Melissa Tran,
  • Ash Patel

摘要

Purpose

We analyzed plastic surgery applicants in the Texas Seeking Transparency In Application to Residency (STAR) database (2021–24) to assess how number of interview offers (NIO) relates to match outcomes and how this may affect signaling strategy.

Methods

Multivariable logistic regression compared NIO with other objective factors such as Step scores, and regression models were fit for NIO vs. match probability. Applicants were stratified as pre-signal implementation (2021–2022) or post-signal (2023–2024).

Results

248 applicants were included, of which 182 (73.4%) matched. The median NIO was 12 ± 9.5, and NIO significantly predicted matching (p < 0.01). The maximum probability of matching (89.3% match probability) was predicted at 26.6 NIO. Applicants with less than 12 NIO had significantly lower match rates (60.4%) than those with 12–27 NIO (82.9%) or greater than 27 NIO (88.5%) (p < 0.01). In the < 12 NIO group, no variables distinguished matched from unmatched applicants. Median NIO (p = 0.08) and mean NIO of matched applicants (p = 0.09) were similar pre-signal and post-signal. However, the impact of NIO on match chances significantly differed between pre-signal and post-signal groups by Z-statistic (p < 0.01), with NIO predictive of successful match only in the post-signal group (OR 1.31 [1.13, 1.51], p < 0.01). The minimum NIO for 90% chance of matching was 47.5 in the pre-signal group and 14.5 in the post-signal group.

Conclusions

The implementation of preference signaling may increase the importance of each interview and alter NIO thresholds for a successful match. Our findings suggest increasing plastic surgery signals to 25–30 may promote thoughtful applications while maintaining strong match outcomes.